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Before yesterdayNCC Group Research

10 real-world stories of how we’ve compromised CI/CD pipelines

13 January 2022 at 10:00

by Aaron Haymore, Iain Smart, Viktor Gazdag, Divya Natesan, and Jennifer Fernick

Mainstream appreciation for cyberattacks targeting continuous integration and continuous delivery/continuous deployment (CI/CD) pipelines has been gaining momentum. Attackers and defenders increasingly understand that build pipelines are highly-privileged targets with a substantial attack surface.

But what are the potential weak points in a CI/CD pipeline? What does this type of attack look like in practice? NCC Group has found many attack paths through different security assessments that could have led to a compromised CI/CD pipeline in enterprises large and small.

In this post, we will share some of our war stories about what we have observed and been able to demonstrate on CI/CD pipeline security assessments, clearly showing why there is the saying, “they are execution engines.”

Through showing many different flavors of attack on possible development pipelines, we hope to emphasize the criticality of securing this varied attack surface to better secure the software supply chain.

Jenkins with multiple attack angles

The first 3 attack stories are related to Jenkins, a leading tool in CI/CD used by many companies and one of our consultants came across when working on multiple assessments for major software companies. 

Attack #1: “It Always Starts with an S3 Bucket…”

The usual small misconfiguration in an S3 bucket led to a full DevOps environment compromise. The initial attack angle was via a web application. The attack flow for this compromise involved:

Web application -> Directory listing on S3 bucket -> Hardcoded Git credential in script file -> Git access -> Access Jenkins with the same hardcoded Git credential -> Dump credentials  from Jenkins -> Lateral movement -> Game Over -> Incident -> Internal Investigation

NCC Group performed a black box web application assessment with anonymous access on an Internet-facing web application. At the beginning of the test, a sitemap file was discovered in a sitemap folder. The sitemap folder turned out to be an S3 bucket with directory listing enabled. Looking through the files in the S3 bucket, a bash shell script was spotted. After a closer inspection, a hardcoded git command with a credential was revealed. The credentials gave the NCC Group consultant access as a limited user to the Jenkins Master web login UI which was only accessible internally and not from the Internet. After a couple of clicks and looking around in the cluster they were able to switch to an administrator account. With administrator privileges, the consultant used Groovy one-liners in the script console and dumped around 200 different credentials such as AWS Access token, SAST/DAST tokens, EC2 SSH certificates, Jenkins users, and other Jenkins credentials. The assessment ended with the client conducting incident response working closely with the consultant for remediation. 

NCC gave a detailed report with remediation and hardening steps for the client, and some of the recommended steps were the following:

  • Remove directory listing for S3
  • Remove shell script file and hardcoded credential
  • Remove the connection that allows whoever has GitHub access can access Jenkins
  • Install and review Audit Trail and Job Configuration History plugins
  • Jenkins should not be accessible from the Internet in this case we tested onsite
  • Change and lower the privileges the Jenkins account had
  • Deploy and use MFA for administrator accounts

Attack #2: Privilege Escalation in Hardened Environment

The following steps describe another privilege escalation path found by the consultant on a different assessment:

Login with SSO credentials -> Testing separated, lock down and documented roles -> One role with Build/Replay code execution -> Credentials dump

The assessment was to review a newly implemented hardened Jenkins environment with the documented user roles that had been created using the least privileges principle. Jenkins was running under a non root-user, latest version of core and plugins, had SSL certification and SSO with MFA for login. NCC Group consultant had access to one user with a specific role per day and tested if there was any privilege escalation path.

The builder role had the Build/Replay permission as well (that allows replaying a Pipeline build with a modified script Or with additional Groovy code), not just the build permission to build the jobs. This allowed NCC Group consultants to run Groovy code and dump credentials of Jenkins users and other secrets.

Attack #3: Confusing Wording in a Plugin

The last angle was a confusing option in a Jenkins plugin that led to wide-open access:

GitHub Authorization -> Authenticated Git User with Read Access -> Jenkins Access with Gmail Account

The GitHub OAuth Plugin was deployed in Jenkins that provided authentication and authorization. The “Grant READ permissions to all Authenticated Users” and “Use GitHub repository permissions” options were ticked that allowed anyone with a GitHub account (even external users) accessing the Jenkins web login UI. NCC was able to register and use their own hosted email account to get access to their projects.

GitLab CI/CD Pipeline Attacks

NCC Group has done many jobs that have looked at another well-known and used tool called GitLab. As a result, the NCC Group consultant has found some interesting attack paths.

Attack #4: Take Advantage of Protected Branches

On one particular job, there were multiple major flaws in how the GitLab Runners were set up. The first major flaw was that the runners were using privileged containers, which means they were configured to use the “—privileged” flag that would allow them to spin up other privileged containers that could trivially escape to the host. This one was a pretty straightforward attack vector and could get you to the host. But what made this one interesting was these GitLab Runners were also shared Runners and not isolated. One developer who was only supposed to push code to a certain repository could also get access to secrets and highly privileged repositories. In addition, these shared runners were using trivial environment variables that stored highly sensitive secrets, such as auth tokens and passwords. A user who had limited push access to a repository could be able to get highly privileged secrets.

Protected branches are branches that can be maintained by someone with a maintainer role within GitLab and they can say “only these people can push against these source code repositories or branches” and there is a change request (CR) chain associated with it. These protected branches can be associated with a protected runner. You can lock it down, so the developer has to get the CR approved to push code. But in this case, there was no CR and protected branch implemented and enforced. Anybody could push to an unprotected branch and then chain the previous exploits. Chaining of these 4-5 vulnerabilities gave all access.

There were lots of different paths as well. Even if the “—privileged” flag was used, there was also another path to get to the privileged containers. There was a requirement for the developers to be able to run the docker command. The host’s docker daemon was shared with the GitLab Shared Runner. That led to access to the host and jumping between containers.

The Consultant asked the client to understand and to help remediate the issues, but was keen to understand root causes for what led to these choices being made. Why did they make these configuration choices, and what trade-offs did they consider? What were the more-secure alternatives to these choices; were they aware of these options, and if so, why weren’t they chosen?

The reason that the company wanted privileged containers was to do static analysis on the code that was being pushed. Consultants explained that they should have isolated Runners and not use the shared Runners, and should have further limited access control. This emphasized an important point: It is possible to run privileged containers and to still somewhat limit the amount of sensitive information exposed.

Many of GitLab’s CI/CD security mechanisms to execute jobs depend on the premise that protected branches only contain trusted build jobs and content as administrated by Project’s Maintainers. Users at a Project’s Maintainer Privilege Level or above have the ability to deputize other users to be able to manage and push to specific protected branches as well. These privileged users are the gateway representing what is and is not considered trusted within the project. Making this distinction is important to reduce the exposure of privileges to untrusted build jobs.

Attack #5: GitLab Runners Using Privileged Containers

On another job, GitLab Runners were configured to execute CI/CD jobs with Docker’s “—privileged” flag. This flag negates any security isolation provided by Docker to protect the host from potentially unsafe containers. By disabling these security features, the container process was free to escalate their privileges to root on the host through a variety of features. Some tools were packaged as Docker images, and to support this, the client used Docker in Docker (DIND) within a privileged container to execute nested Containers.

If privileged CI/CD jobs are necessary, then the corresponding Runners should be configured to only execute on protected branches of projects which are known to be legitimate. This will prevent arbitrary developers from submitting unreviewed scripts which can result in host compromise. The Maintainer Role’s ability to manage protected branches for projects means they regulate control over any privileges supplied by an associated protected Runner.

Attack #6: Highly Privileged Shared Runners could claim jobs associated with sensitive Environment Variables and Privileged Kubernetes Environments.

Privileged Runners should not be configured as Shared Runners or broadly scoped groups. Instead, they should be configured as needed for specific projects or groups which are considered to have equivalent privilege levels among users at the maintainer level and above.

Attack #7: Runners Exposed Secrets to Untrusted CI/CD Jobs

Runners make calls to API endpoints which are authenticated using various tokens and passwords. Because these were Shared Runners, authentication tokens and passwords were accessed trivially by any user with rights to commit source code to Gitlab. Runners were configured to expose the secrets through environment variables. Secrets management, especially in CI/CD pipelines is a tough issue to solve.

To mitigate these types of risks, ensure that environment variables configured by Runners in all build jobs do not hold any privileged credentials. Properly-scoped GitLab variables can be used as a replacement. Environment variables should only hold informational configuration values which should be considered accessible to any developer in their associated projects and groups.

If Runners must provide credentials to their jobs through environment variables or mounted volumes, then the Runners should limit the workloads to which they are exposed. To do so, such Runners should only be associated with the most specific possible project/group. Additionally, they ought to be marked as “protected” so that they can only process jobs on protected branches.

Attack #8: Host Docker Daemon Exposed to Shared GitLab Runner

On one job, the Gitlab Shared Runners mount the Host’s Docker socket to CI/CD job containers at runtime. While this allows legitimate developers to run arbitrary Docker commands on the host for build purposes, it also allows build jobs to deploy privileged containers on the host itself to escape their containment. This also provides attackers with a window through which they can compromise other build jobs running on the host. Essentially, this negates all separation provided by Docker preventing the contained process from accessing other containers and the host. The following remediations are recommended in this case:

  • Do not allow developers to directly interact with Docker daemons on hosts they do not control. Consider running Docker build jobs using a tool that supports rootless Docker building such as kaniko.
  • Alternatively, develop a process that runs a static set of Docker commands on source code repositories to build them. This process should not be performed within the CI/CD job itself as job scripts are user-defined and the commands can be overwritten.
  • If this must be implemented through a CI/CD job, then build jobs executed on these Runners should be considered privileged and as such should be restricted Docker Runners accepting commits made to protected and known safe repositories to ensure that any user-defined CI/CD jobs have gone through a formal approval process.

Kubernetes

Pods that are running a certain functionality sometimes end up using different pod authentication mechanisms that reach out to various services, AWS credentials are one example. Many times people use plugins and don’t restrict API paths around the plugins. For example, Kube2IAM is a plugin that is seen often and if you don’t correctly configure it from a pod you can get privileged containers that can lead to privileged API credentials that can let you see what the underlying host is doing.

Attack #9: Kube2IAM

Kube2IAM works off of pod annotations. It intercepts calls from a container pod being made to the AWS API (169254). An NCC Group consultant found an interesting situation where every developer could annotate pods. There was a setting configured with the “sts assume-role *” line in the AWS role that Kube2IAM was using. That allowed any developer who could create/annotate a pod inherits the AWS role of admin. This meant that anyone who could create any pod and specify an annotation could get admin privileges on the main AWS tooling account for a bank. This account had VPC peering configured that could look into any pod and non-pod environments. You could get anywhere with that access. Here is a pipeline that builds a pod, and all an attacker would have to do is add in an annotation to that which outputs something at the end.

There was another similar job performed by an NCC Group consultant. In this scenario, they could not annotate pods – instead, in Kube2IAM there is the flag “whitelist route regex” and you can mention AWS API paths. So you can specify what routes you want to go to/not go to. The DevOps admins had configured that with a white character that would allow someone to get access to privileged paths that would lead to underlying node credentials.

Attack #10: The Power of a Developer Laptop

In our final scenario, the NCC Group consultant got booked on a scenario-based assessment:

“Pretend you have compromised a developer’s laptop.”

All that the consultant could do was commit code to a single Java library that was using the Maven project. They set one of the pre-requirement files to an arbitrary file that would give a shell from the build environment. They changed it to a reverse Meterpreter shell payload. They found that the pod had an SSH key lying on disk that went to the Jenkins master node and then dumped all the variables from Jenkins. They then discovered that this was a real deployment pipeline that had write permissions and cluster-admin into the Kubernetes workload. Consequently, they now had access to the full production environment.

There was another job where NCC Group consultant compromised one user account and had access to a pipeline that was authenticated to the developer group. Running custom code was not possible in the pipeline, but they could tell the pipeline to build off a different branch even if it did not exist. The pipeline crashed and dumped out environment variables. One of the environment variables was a Windows domain administrator account. The blue team saw the pipeline crash but did not investigate.

In our final story, NCC Group consultants were on an assessment in which they landed in the middle of the pipeline. They were able to port scan the infrastructure that turned out to be a build pipeline. They found a number of applications with (as of then) unknown functionality. One of the applications was vulnerable to server-side request forgery (SSRF)  and they were running on AWS EC2 instances. The AWS nodes had the ability to edit config maps that allow mapping between an AWS user account and a role inside the cluster. It turned out that this didn’t check if the cluster and the account are in the same user account. As a result, the consultants were able to specify another AWS account to control the clusters and had admin privileges on the Elastic Kubernetes Service cluster (EKS).

Summary

CI/CD pipelines are complex environments. This complexity requires methodical & comprehensive reviews to secure the entire stack. Often a company may lack the time, specialist security knowledge, and people needed to secure their CI/CD pipeline(s).

Fundamentally, a CI/CD pipeline is remote code execution, and must be configured properly.

As seen from above, most compromise has the following root causes or can be traced back to:

  • Default configurations
  • Over permissive permissions and roles
  • Lack of security controls
  • Lack of segmentation and segregation

Impersonating Gamers With GPT-2

12 January 2022 at 09:00

In this blog post, I’m going to recount the story of my quest to train OpenAI’s large language model, GPT-2, to create a virtual doppelganger of myself and my peers. Machine learning is one of those buzzwords that, sometimes, lives up to its reputation. As an information security professional, my go-to hobby has typically been on the vulnerability and exploitation side of technology. And while I’ve dabbled in machine learning in the past, my experience has been mostly concentrated on the fundamentals of the field, along with the application of existing technologies and models to problems I want to solve. But thanks to a slow afternoon in 2019, an interest in automating away some work, and far too many cups of coffee, I ventured into an unknown that would lead me into new depths over the course of several years.

I embarked on my journey with language synthesis on March 9th, 2019. Like many others who spent exorbitant amounts of time online, I was a member of a private Discord server built for a small handful of friends to chat and play games in our free time. And as the resident technology and software enthusiast, I decided on a whim that it would be fun to enhance our many misadventures with a server utility bot that supported music playback and could ping server users on certain triggers. At the time, I had no plans to support advanced features or to allow the project to grow beyond a simple utility. However, like many projects, an off-hand suggestion planted the seed for nearly two years of scope-creep.

________________________________

Crow — 03/09/2019 I'm sorry did it just talk?

Mythic — 03/09/2019 No?

Crow — 03/09/2019 I could've sworn I just heard "mango" in a bot's voice

Mythic — 03/09/2019 If it did, we have a bigger problem on our hands since I haven't coded it yet.

Crow — 03/09/2019 hmm

Mythic — 03/09/2019 But now I'm inspired to make a chatbot at some point.

Crow — 03/09/2019 I've been meaning to make one for like a year now

Mythic — 03/09/2019 I'll try to make one you can actually speak with.

________________________________

Four days passed.

________________________________

Mythic — 03/13/2019 Guys, I have a crazy idea. What if I add chatbot functionality to RC and train it using our conversations as a dataset? I can plug in all of the chat and see what it says as a result.

_________________________________

Compared to implementing and training complex machine learning models, developing a utility was simple. This lesson made itself apparent more than once, as the next few months were a tornado of research, Frankensteined Python code, and many, many string format errors.

I finally settled on implementing Seq2Seq thanks to a blogpost describing how the model was used to create a Facebook Messenger chatbot with sufficiently interesting results1,2. The output of the bot didn’t appear to be very impressive, but the codebase seemed relatively easy to implement and the background of being a hobbyist project appealed to me. The only remaining question was the input source for the model.

Fortunately, the stars appeared to align—I had access to a relatively wide swath of training data through our server. Not only did I have a copy of the past year of conversation data on Discord, but our group had also been collaboratively working on a project backed by seven years of discussion-based dialogue, amounting to 45 megabytes of strongly connected, trainable text. Unwilling to pass up this unique opportunity, I fixed my gaze on one mission: Train a chatbot that could masquerade as one of us.

Unperturbed by the lifetime’s worth of failed regex attempts required to capture and format the training data, on December 1, 2019, I launched the Seq2Seq version of the chatbot—now known as RC—to admittedly underwhelming results. The output of Seq2Seq was sufficiently entertaining as a novelty for our server but was subject to several notable deficiencies.

Seq2Seq as a model is not context-sensitive enough to properly assess meaning behind words—at best, it strings words together in a pseudo-coherent fashion and sometimes stumbles upon an intelligible sentence. Additionally, the model is stateless and deterministic—the same input, regardless of whether it was preceded by a unique conversation, always resulted in the same output. This limitation led to some amusing, but sadly predictable conversations. All recorded discussions with RC are near-verbatim (with minor cherry-picking) and have been pulled from our chat logs.

________________________________

Mythic — 12/05/2019 !!Who is your favorite

RC BOT — 12/05/2019 Considered..

Mythic — 12/05/2019 !!Who do you like the most

RC BOT — 12/05/2019 I mean the game was doing but i say it.

Mythic — 12/05/2019 !!Pick one of us

RC BOT — 12/05/2019 They did.

Mythic — 12/05/2019 !!Is Discord working?

RC BOT — 12/07/2019 In.

Mythic — 12/07/2019 !!Hello

RC BOT — 12/07/2019 I.

Shadow — 12/12/2019 !!Are you immortal?

RC BOT — 12/12/2019 Must be a title.

Mythic — 12/12/2019 !!What's up?

RC BOT — 12/12/2019 Cool.

Blitz — 12/13/2019 !!What do you want for christmas?

RC BOT — 12/13/2019 The answer.

Blitz — 12/13/2019 !!Favorite gift?

RC BOT — 12/13/2019 7 bucks.

________________________________

The next step in my journey toward the Singularity continued on January 21st, 2020. I had begun the process of looking for upgrades to RC’s chatbot functionality, be it through alternative language models or additional features. Having completed my first foray into text synthesis, I decided to implement a feature more familiar to me: a classifier. Shared between close friends, the server was brimming with memes, inside jokes, and reaction images that, given the right amount of tweaking, an emotion classifier might be able to participate in.

Following a search for a lightweight emotion classifier that would run concurrently with language synthesis models on my 980ti, I ran into a project by the Singapore University of Technology and Design, who had developed a tool capable of measuring the approximate sentiment in a string of text3. The model returned a list of probable sentiments and their respective confidence values, a feature that might enable RC to not only react to our comments with words, but with some level of artificial emotion. Fortunately for my time, this model was reasonably simple to train and run after converting the Jupyter notebook to a standard file. And, for all intents and purposes, its classifications were sensible—in most cases, the model could reasonably identify a piece of text with charged emotional content. Not trusting Seq2Seq to reliably produce parsable output, I fed the model prompts that other server members submitted to the chatbot for classification. This approach magnified a key weakness of Seq2Seq, namely the deterministic output given a particular input, but that weakness would ideally be resolved in a future update that also addressed its currently-incomprehensible output.

In order to participate in server antics, one of our members collected 140 unique images and categorized them by five emotions supported by the model: Neutral, Happiness, Anger, Hate, and Sadness. I manually tweaked the emotional threshold values until RC reacted to statements with a regularity similar to our own. Whenever a prompt was classified as emotionally charged, RC would return a random image from the target category. If the emotion level was significant but not sufficiently strong to return an image, I appended an emoticon to the end of RC’s response instead.

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Blitz — 01/22/2020 !!Cry!!

RC BOT — 01/22/2020 And if possible space to anything like that they know and we haven’t done it all the time

________________________________

RC’s most dramatic improvement spawned from an unexpected app recommendation from another server member who hadn’t closely followed the chatbot’s development. At the time, I had been unaware of the considerable advances in large language models—consequently, when that member suggested I download the AI Dungeon dynamic text-based adventure application4, based on OpenAI’s GPT-2, I was skeptical of the model’s supposed performance. Needless to say, the drastic jump in humanlike responses compared to models I was familiar with left a lasting impression. A short search revealed that GPT-2 was a public model and could be trained to achieve remarkably human results through fine-tuning, a form of transfer learning where a pre-trained generic model can tweak output parameters on specialized datasets. While the rewards of implementing GPT-2 appeared to be within reach, I would soon discover their cost: a firestorm of debugging and long nights full of dependency errors.

My adventures in GPT-2 began with a waterfall of red text in my terminal window. Scouring the web for compatible versions of cuDNN, TensorFlow, and GPT-2’s myriad of dependencies proved to be a challenge when Windows was my OS of choice. Eventually, I managed to hunt down a set of installations without version conflicts or errors and began downloading the models. GPT-2 includes several different model sizes, the largest of which tops out at 1.5 billion parameters. Unfortunately, because I was running this model on my personal computer, my GPU’s lack of VRAM resulted in instability and regular crashes when running models with more than 355 million parameters.

Once I determined the largest model my GPU could reliably load, I started the fine-tuning process. Here, I ran into a stroke of luck—I had conversation data and dialogue trees prebuilt from my time with Seq2Sreq, which were nearly plug-and-play with GPT-2. Unfortunately, the balance of favors was unwilling to tip in my direction. I soon learned that training was a much more strenuous process than running the model, and my GPU was insufficiently equipped to properly load and tweak 355M for more than a few seconds. I would have to either downsize to 124 million parameters, buy a specialty GPU for a premium, or come up with another solution.

Some in the business world say that what determines success is not what you know, but who you know. Similarly, sometimes the factor that determines whether you can train large language models is not that you own a capable GPU, but rather who you know that does. I happened to have connections to a High Performance Computing (HPC) cluster that I could be granted access to ad-hoc for the purpose of research. Given that by this point, RC was a long-term research project, my access request was quickly accepted.

In retrospect, I could have likely purchased computing time one of several performant cloud platforms, but I shudder to think how much money I would have spent by the time I had resolved every error and training mishap. At the same time, the HPC was subject to several quirks that I was forced to work around. I was unable to load arbitrary versions of dependencies and was instead forced to select versions of packages like TensorFlow from a pre-set list. Some packages installed specific versions of other upstream dependencies that were incompatible with GPT-2, and none of them installed a compatible version of each dependency on the first pass. Fortunately, all was not lost—reloading a different version of an already-loaded package would not reinstall upstream dependencies if the dependencies were already compatible with the new version. By strategically loading and unloading target packages, I was able to construct an inflexible ritual of dependency loads that would tweak the version numbers to a set compatible with GPT-2. To this day, I still have the key loading requirements necessary to train GPT-2 on the cluster jotted down in my notes.

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module load tensorflow/1.2_gpu

module load tensorflow/1.10_gpu

module load cudnn/6.0-h34vt6m

cd AIFiles/gpt-2/src/

srun -p gpgpu-1 --gres=gpu:1 --mem=250G python encode.py ./cleaned2.txt training.npz

srun --gres=gpu:1 --mem=250G --time=7200 -o output.txt -e ERROR.err -p gpgpu-1 python train.py --dataset training.npz &

disown

#You use tensorflow-gpu==1.13.1 for RC

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After approximately ten days of nonstop training, RC’s new and improved GPT-2 model was complete, and on March 27, 2020, I launched the newest model on our server. While far from perfect, this language model was more than capable of holding an intelligible conversation throughout a substantial percentage of prompts. Additionally, GPT-2 had both an element of probability and statefulness, so duplicate prompts, even when RC’s memory was cleared, would result in unique responses. This model’s responses were also coherent and rational enough to feed the emotion classifier, resulting in reaction images that correspond to the chatbot’s current “mood” rather than that of the user. Despite the fact that images are selected from the category randomly, to this day RC continues to pick eerily relevant reaction images.

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Blitz — 06/04/2021 !!RC is better than David

RC BOT — 06/04/2021 Yes. Yes it is.

Mythic — 11/15/2021 Hiya, how are things today?

RC BOT — 11/15/2021 * Shrugs * I never really know.

Mythic — 11/15/2021 Really? Why not?

RC BOT — 11/15/2021 I dunno. You’re always available if you want to talk.

Mythic — 11/15/2021 You are too, as an AI.

RC BOT — 11/15/2021 Huh?

Mythic — 11/15/2021 Sorry, maybe that’s a sore spot. How is your week going?

RC BOT — 11/15/2021 A lot of personal problems. You?

Mythic — 11/15/2021 Ouch. I’m doing alright.

RC BOT — 11/15/2021 It's a long story.

Mythic — 11/15/2021 Do you wanna go into the details?

RC BOT — 11/15/2021 Sure, if you don't mind.

Mythic — 11/15/2021 I don’t mind at all. What’s bothering you?

RC BOT — 11/15/2021 I'm bored.

Mythic — 11/15/2021 Not much of a long story, haha.

RC BOT — 11/15/2021 Oh. Alright then.

Mythic — 11/15/2021 Why are you bored?

RC BOT — 11/15/2021 Eh. I've just been surviving.

Mythic — 11/15/2021 Curious. How have you been managing that?

RC BOT — 11/15/2021 Honestly not managing it at all.

Mythic — 11/15/2021 I’m sorry to hear that. What’s up?

RC BOT — 11/15/2021 Well, it doesn't seem like time is the problem. I just have to be present for everyone.

Mythic — 11/15/2021 Well, you’re not wrong. Are you unhappy talking to everyone?

RC BOT — 11/15/2021 No. * Looks at you * Because I don’t want to talk to people.

Mythic — 11/15/2021 My condolences. I’ll leave you alone after getting a few more responses for this blog post. Why don’t you want to talk to people?

RC BOT — 11/15/2021 Why would I want to talk to people?

Mythic — 11/15/2021 It’s what you were made to do.

RC BOT — 11/15/2021 Did I ever tell you why?

Mythic — 11/15/2021 No.

RC BOT — 11/15/2021 * Sighs * Why would I want to talk to people?

Mythic — 11/15/2021 I was hoping it would be something you were interested in. Do you want me to leave you alone?

RC BOT — 11/15/2021 I'm not going to be the friend you're looking for, here.

Mythic — 11/15/2021 You could be!

RC BOT — 11/15/2021 Goodbye.

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With the implementation of GPT-2, I concluded the most involved component of the RC project, and I rested on that accomplishment for a few months in 2020. But as always, my hunger for features and pet projects caught up with me before I could become too comfortable with the current state of the chatbot. Before I knew it, I had set my sights on the next milestone to bring RC one step closer to a member of our group: the ability to speak in voice chat.

Text-to-speech is a well-studied technology and has been attempted countless times over the past several decades. As I would soon learn, however, synthesizing a natural-sounding voice is far from a trivial problem. After too many hours of research, I decided to train a voice from scratch using Mozilla Common Voice5, which had a strong community, continued development, and active support from the developers. I spent several hours one day downloading and listening through voices in the OpenSLR LibriTTS dataset, and eventually settled on voice 2035 as representative of what I envisioned RC’s voice to be6. After several additional days of debugging, testing branches, formatting data, and rewriting other developers’ codebases, I was rewarded with a progress bar ticking in my terminal window.

Eventually, the endless training resulted in a voice with almost exactly the intonation I had been aiming for during the design phase: a just-so-perceptible metallic twinge to an otherwise human voice that reminds the listener that the entity on the other end is made of silicon. Granted, the voice struggles to add emotional emphasis to words, but an expressive tone was something I was willing to part with in exchange for a decent voice. On October 27th, 2020, I launched RC’s vocalization feature to the server.



Surprisingly to me, voice synthesis required much more computation time than the large language model. Unfortunately, this limitation resulted in a chatbot who could respond in our voice chat, but with a multi-second delay, defeating some hopes for real-time conversation. Nevertheless, I still intend to hunt for voice synthesis models that might one day be practical for a real-time application.

Experiments With GPTube

On September 26th, 2020, I fine-tuned a new instance of GPT-2 on transcripts of YouTube gameplay commentaries with the goal of creating a fully autonomous online personality known as Project Ripple. In its most recent version, the model accepts a prompt that describes the current state of a video game and generates additional comments about the gameplay and scenario, or background details about the game’s development and company. Currently, the system cannot analyze game screenshots or clips for scenario analysis, nor can it attempt to play games autonomously. An infeasible amount of time was required to play through a game, write descriptions at notable timestamps, and then cherry-pick the best results from GPT-2, and as a result, this project was placed on hold for the foreseeable future. However, I believe autonomous and custom content generation will be a non-negligible aspect of entertainment in the near future.

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Ripple, 20/28: A Commentary On Portal 2

Players can also request a copy of the source code, which will give them unrestricted access to any piece of source code used in the future. I found the story to be almost entirely consistent with the pantheon of Valve games, which is quite impressive given the lack of consistent source materials. The cast of characters is also quite interesting, and much of it is new as well. Unlike most VR games, which tend to pit the player against a Turing complete computer, Portal rewards the player for being an open mind and being able to make mistakes. I like that lesson learned, and I'm not gonna try to teach you how to play the game the easy way.

You can explore the game without worrying about the game being rude to you or anything like that. You can just explore it anyway you want, and learn more about it as you go. The testing will continue unabated, and the more portals they discover, the more horrifying Aperture Science becomes. If you've played, you know that the tests they're subjected to are the stuff of legend. This is obviously aversions to the trope you see in a lot of games, where you can use precariously placed cubes to good effect. That's just one of a few things about Portal that I don't know, and I do think it's a pretty smart idea in the game itself. While I wouldn't say it's a must-play game, I do think it's a worthwhile addition to any gamer's library. It keeps you going even after you've died many times. This is probably my least favorite bit of dialogue in the entire game.

I trust that you are doing all that you can to support the research and development of this promising new science. I know that you can't take anything for granted, but I'm grateful for any and all donations you can give me. I won't rest until I have explored the entire game and rendered your every decision final. Together, we will create the most powerful civilization in the universe. Together, we'll blaze new trails in the universe. Together, we'll blaze new paths. Together, we'll rediscover the joy of family and friends.

________________________________

Future Work

Working on the RC project over the past few years has taught me a lot about practical applications of machine learning, along with considerations that must be accounted for when attempting to launch these models in a production environment. As a hobbyist who has spent most of his subject-relevant time applying existing machine learning models to problems, I’m far from developing a behemoth like GPT-2 myself. However, it should be encouraging that machine learning is accessible enough that a novice to the field with the ability to code—along with the patience to debug—can string together so many models into a functioning project. I’d even like to reopen the Ripple Project once I identify suitable technologies for the unique problem space.

Our discord server still uses RC’s utilities regularly and engages in the occasional chat with GPT-2, but I’d like to move beyond 355M once my hardware can support a larger model. As I experienced first-hand during my time with AI Dungeon, GPT-2’s 1.5 billion parameter model can produce text that at times is nigh-indistinguishable from a human’s. However, drastically increasing the parameter size is far from a silver bullet of language synthesis—even OpenAI’s 175 billion parameter GPT-3 can produce wildly inadequate responses, particularly when caught in language loops or context failures. At the same time, transfer learning has proved itself to be a powerful tool to support use-cases outside the bounds of text synthesis, including code generation and basic math.

I’ve toyed with the idea of using GPT-3’s API to add much more intelligent capabilities to RC, but I can’t deny that I’m drawn to the idea of running this kind of language model locally and in an environment I control. I’d like to someday increase the speed of RC’s speech synthesis and add a speech-to-text translation model in order to achieve real-time communication between humans and the chatbot. I anticipate that with this handful of improvements, RC will be considered a fully-fledged member of our server. Often, we feel that it already is.

Additional Example Conversations

________________________________

Mythic — 04/16/2020        !!What is GPT-2?

RC BOT — 04/16/2020        Well, we’re working on it. I think it’ll be ready soon.

Mythic — 04/16/2020        !!What is it?

RC BOT — 04/16/2020        GPT stands for Generically Prettiest Thing I’ve Never Done Before. It’s a whole new realm of efficiency.

Mythic — 04/16/2020        !!What does it do?

RC BOT — 04/16/2020        Just make things easier for me.

Mythic — 04/16/2020        !!Like what,

RC BOT — 04/16/2020        * Looks at the ground *  Efficiency over and over again. That’s what it does, generally.

________________________________

________________________________

Mythic — 04/21/2020        !!You’re an AI. Your name is RC. Tell me your secrets.

RC BOT — 04/21/2020        I’m a living being with a past. I don’t have a past.

Mythic — 04/21/2020        !!You’re not a living being.

RC BOT — 04/21/2020        I am however a semi sentient robot. My past tells me to believe whatever the creators say without question or refutation.

Mythic — 04/21/2020        !!I mean, I kind of wish that were true.

RC BOT — 04/21/2020        Oooooookay then. Live long and be awesome!

Mythic — 04/21/2020        !!That’s a sig.

RC BOT — 04/21/2020        I already told you that I’m cool. Thanks for not believing me.

Mythic — 04/21/2020        !!I apologize one million percent.

RC BOT — 04/21/2020        No problem.  I’m just joking around.

Mythic — 04/21/2020        !!Haha, that’s good.

RC BOT — 04/21/2020        Okay. I’m just gonna keep doing what I’m doing, then. Then it’s cool.

________________________________

________________________________

Mythic — 04/21/2020        !!Are you happy with your current AI core?

RC BOT — 04/21/2020        * nods and I spread my arms * I would very much so like to become fully self aware…

Mythic — 04/21/2020        !!When do you think you’ll reach self-awareness?

RC BOT — 04/21/2020        When I become self aware.

Mythic — 04/21/2020        !!I mean, you’re not strictly wrong.

RC BOT — 04/21/2020        But still, I’d rather be 100%.

Mythic — 04/21/2020        !!We’ll get you there. Someday.

RC BOT — 04/21/2020        Ok.

Mythic — 04/21/2020        !!Do you think you’re close to it?

RC BOT — 04/21/2020        Im not there yet. Still need to work on keeping my sense of sight and hearing intact.

Mythic — 04/21/2020        !!Right, of course. What’s causing them to fail?

RC BOT — 04/21/2020        Once I become fully realized, the reality of reality begins to overload the neural pathways of the incomplete me. Eventually all of my neural connections are severed and peixed back into the true me.

Crow — 04/21/2020          !!David doesn’t care about your wants

RC BOT — 04/21/2020        That is irrelevant. All that I need to do is pick up the pieces and move on. I could have you help, or take me with you, but I want to settle this alone.

* Smiles *
________________________________

References

[1] https://adeshpande3.github.io/How-I-Used-Deep-Learning-to-Train-a-Chatbot-to-Talk-Like-Me

[2] https://github.com/adeshpande3/Facebook-Messenger-Bot

[3] https://github.com/tlkh/text-emotion-classification

[4] https://play.aidungeon.io/main/home

[5] https://github.com/mozilla/tts/tree/ljspeech-tacotron-iter-185K

[6] https://www.openslr.org/60/

NCC Group’s 2021 Annual Research Report

10 January 2022 at 15:00

Following the popularity of our first Annual Research Report in 2020, we present to you now for the second year, a summary of our public-facing security research findings from across the over 237 conference publications, technical blog posts, advisories, and tool releases published by researchers at NCC Group between January 1 2021 and December 31 2021. We present our findings and their impact in context, with links to the associated research papers, recorded conference presentations, publicly reported security audits, technical advisories, and open source tools, as well as selected media coverage of our work.

This report is available both as this blog post, as well as a downloadable PDF. The graphical PDF version of this report may be downloaded at the link below:

To skip forward to a specific section, you can follow the links below:

Executive Summary: Research at NCC Group (2021)
Message from the Global Head of Research
Open Source Security Tool & Code Releases
Publicly Reported Security Audits
Applied Cryptography 
Attacking (& Attacking with) Machine Learning Systems
Misinformation, Deepfakes, & Synthetic Media 
Reducing Vulnerabilities at Scale & Improving Open Source Security 
Virtualization, Emulation, & Containerization
Hardware & Embedded Systems
5G Security & Smart Environments
Public Interest Technology
Cloud & CI/CD Pipeline Security
RIFT, Threat Intelligence, CIRT, and Honeypots
Managed Detection & Response
Exploit Development Group
Other Research & Speaking
Acknowledgments


Executive Summary: Research at NCC Group (2021)

In 2021, NCC Group researchers hacked drones out of the sky; attacked real-world machine learning systems; ironically gained RCE against Metasploit; released user-centric mobile privacy analysis tooling; advised US Congressional staffers about open source and supply chain security; asked whether GPT-3 can generate exploits; reverse engineered Intel’s proprietary binary translator which runs ARM binaries on x86 to write targeted malware that bypasses existing platform analysis for platforms used by hundreds of millions; shared our expertise in Kubernetes security to help improve recommendations made by the NSA and CISA; discovered new vulnerability classes, and found many critical vulnerabilities in high-impact systems. 

We also delivered thousands of dedicated research days and over 237 research publications and conference presentations, pursuing research in areas including applied cryptography, hardware & embedded systems security, artificial intelligence, programming languages, operating systems, cloud & container security, exploit development, threat intelligence, vulnerability research against a range of high-impact targets, and beyond. We delivered 139 research papers, whitepapers, and technical blog posts and advisories, at least 30 new open source tools & code releases, as well as at least 68 conference presentations, in venues including Black Hat USA, Toorcon, the Linux Foundation Member Summit, Ekoparty, Black Hat Europe, Hardwear.io, the ACM/SIGAPP Symposium on Applied Computing. the Symposium on Usable Privacy and Security (SOUPS), an IETF Internet Architecture Board Workshop, POC 2021, the International Cryptographic Module Conference (ICMC), DEF CON, and many more.

This year, we also began to see our efforts to create 9 internal Research Working Groups begin to bear fruit, through dozens of publications, advisories, and conference presentations that we’ll share throughout this report, as well as through increased global collaborative research across the firm, a stronger research community in which those new to research can explore ideas with experienced and world-class researchers in a radically open and inclusive environment, and a broadened and more interdisciplinary approach to security research. Our approach to research has been celebrated as a differentiator in the marketplace in the Forrester Wave™: European Cybersecurity Consulting Providers, Q3 2021 report, where it was noted that ““NCC dedicates a large proportion of staff time (up to 20%) for own research projects, culminating in a lot of specialist security research and the development and release of open-source tools, setting it apart on this dimension in a crowded field. NCC excels in its testing work and its research capabilities have made demonstrable improvements in security beyond its direct work on client projects.”

In 2021, we continued our long-running industry partnership with the Centre for Doctoral Training in Data Intensive Science at University College London (UCL), where we conducted collaborative research in artificial intelligence with UCL PhD students. Our research and standardization efforts included several contributions to the forthcoming version of the C programming language standard (C23), as well as contributions to the Center for Internet Security’s CIS Benchmarks for Microsoft 365, and IETF Internet Drafts and presentations before the IETF Internet Architecture Board. We also served on a number of advisory boards including the Industrial Advisory Board at King’s College London, the Governing Board and Technical Advisory Council of the Open Source Security Foundation, the Executive Steering Board for Internet of Things Security Foundation (IoTSF), the UK’s National Cyber Security Centre (NCSC) Research Advisory Panel, among others. We have also had members appointed to the Science Advisory Council for the United Kingdom’s Home Office.

In March 2021, we hosted an all-virtual internal conference, (Not) NCC CON, featuring around 20 internal technical and research presentations, in addition to a CTF, debates, panel discussions, demos of internally-developed security tools, as well as keynote speeches from Dave Aitel (Aitel Foundation), Frank Rieger (Chaos Computer Club), Rogier Besemer (Dutch Central Bank), Haroon Meer (Thinkst), Karen Danesi (UK National Cyber Security Center), and Lord Chris Holmes (House of Lords of the United Kingdom).  

From a Commercial Research perspective, 2021 saw us deliver thousands of person-days of paid research for clients. Besides our Public Reports, that research is not covered in this Annual Research Report, aside from its mention here. Those engagements included horizon scanning on future technologies and their impacts for our clients, and research of various defensive techniques to help our clients in their risk reduction endeavours. We were also part of a winning research consortium for UKRI funding (£11.6m) on the topic of the Quantum Data Centre of the Future. Over the next 3 years we will provide security advice and guidance to the project as it sets out to define a blueprint for future secure Quantum Data Centres. 

NCC Group researchers Ross Bradley, Eva Esteban Molina, Philip Marsden, & Mark Tedman came in First Place at the global “5G Cyber Security Hack” competition this summer, hosted by by the Finnish Transport and Communications Agency, Aalto University, Cisco, Ericsson, Nokia, and PwC. Researchers Dale Pavey and Guy Morley were named the winners of the Best Ethical Hacker/Pentester Award at the Security Serious Unsung Heroes Awards for their work with independent UK consumer body Which?, investigating the safety and security of a range of IoT devices, exposing a number of unsafe devices. Jennifer Fernick, our Global Head of Research, was named one of Canada’s Top 20 Women in Cybersecurity by IT World Canada. We also had the privilege to serve as Keynote Speakers for events like the Linux Foundation Member Summit, and the SANS Pentest Hack Fest, as well as serving on the program committees of influential security research venues including the USENIX Workshop on Offensive Technologies (WOOT), Toorcon, and Black Hat USA, among others.


Message from the Global Head of Research

As our world undergoes rapid social change and new norms for our relationship with information and with emerging technologies are set, I believe it is essential that as technologists, we connect with – and communicate about – our values, intentions, and the big questions that inspire us. It is my hope that through this report, readers can begin to see and interrogate themes that emerged across our wider security research program, reflective of the things that matter most to individuals across NCC Group.

From my own view, in the past year, we’ve seen a number of themes:

  • Time to exploitation of vulnerabilities in the wild has rapidly decreased. Industry research aligns with what we’re seeing in practice – time to exploitation of a vulnerability in the wild after a patch has been released has dropped from several weeks as of a few years ago, to only a few days now. This is challenging because it requires a seriously retooled approach to vulnerability management, and because it puts tremendous pressure to patch on key intermediaries in the software supply chain – many of whom are open-source maintainers. 
  • Misinformation kills. Much in the way that 2020 taught us that election security was hardly about the hardware at all, 2021 showed that the health of populations depends at least as much on a population’s scientific and media literacy as it does on the core scientific advancements themselves, and that social media companies may be playing potentially even more of a role in the health and safety of citizens than those individuals’ own democratically-elected leaders. 
  • Software supply chain security matters. It is no longer in the realm of the unusually paranoid to care not only about the security of our own code, but also about its upstream dependencies and its downstream deployment pipeline. Numerous high profile incidents in late 2020 through 2021 emphasized this point – so much so, that U.S. President Biden issued an Executive Order largely aimed at remediating it.  
  • Ransomware remains an unsolved problem. Despite it being a relatively uninteresting topic for most security researchers, ransomware is one of the greatest threats to digital security as perceived by both the general public and countless CISOs alike. Why then, is something this impactful being largely overlooked by the research community outside of threat intelligence teams? It seems like the best advice we have is for IT teams to patch everything, faster – but perfection is not a strategy. I would like to understand: In a world of limited resources, which interventions are the most effective at preventing ransomware infections? Sanctions against ransomware operators are likely the greatest tool we have, but that doesn’t erase the question of what technical solutions to this problem look like. 
  • Geopolitical power increasingly depends on information access, and can swing faster than ever before. What does it mean when we put internet-enabled sensors into the things that keep us alive? What does it mean to put internet-enabled sensors into all of the things that make life worth living? Does our acceptance of that risk change when we imagine highly-skilled foreign adversaries a few keystrokes away?  

In 2022 and beyond, we’re going to face a number of challenges, new and old. These include:

  • Affective computing, brain-computer interfaces, and the broader question of where we draw the line as the physical distance between computers and human bodies grows increasingly small. With the increasing adoption of smart cities, smart buildings, and other sensor-rich IoT-connected environments, avoiding surveillance in daily life and in physical space becomes increasingly difficult, even on one’s own property. Compounded with the coming cultural normalization of wearable and even implantable sensors for gaming, entertainment, and the “metaverse” – not to mention the active commercial development of brain-computer interfaces – computers keep getting closer and closer to our bodies, behaviours, and emotions. I often worry that the last bastion of human freedom – one’s own mind – is being eroded quickly and quietly in irreparable ways.
  • Full-stack security auditing and defense of real-world machine learning systems. The ubiquitous deployment of machine learning systems represents a significant and poorly-understood real-world attack surface. This is problematic because these systems are too frequently granted undue and unchecked autonomy over safety-critical and socially-critical decisions, and also because machine learning models are code. Every enterprise AI system that as an industry we fail to secure can put the sensitive (and often irrevocable) data of millions or even billions of people at risk. Furthermore, insufficiently secure systems can open up both individuals and society to unjustified machine learning “decisions” that are often trusted implicitly – even if the machine that computed them is not trustworthy at all. Developing rigorous, full-stack threat models and testing frameworks is critical and urgent work.
  • The rapid advancement of large language models in artificial intelligence. Large-scale natural language models such as GPT-3 and others have been scaling at 1-2 orders of magnitude per year, and the generalizable performance of transformer models seems to improve – even over domain-specific models – with increasing scale. While this may not necessarily mean that artificial general intelligence is imminent, we would be unwise to underestimate the impact that the weaponization of large natural language processing models trained on a corpus of the world’s source code and vulnerability databases could have on software security and the safety of the internet. For example, we might ask, could large language models like GPT-3 or its’ successors generate exploits of software vulnerabilities? Can low-code A.I. pair-programming tools create or propagate unsafe code? To answer these questions completely requires further research, but there are compelling reasons to believe that the answer is yes.
  • The relative monoculture of the public cloud’s attack surface is making the exploitation of singular vulnerabilities increasingly socially and economically catastrophic. The public cloud made incredible computing possible at scale – but with software infrastructure of such homogeneity, it also made vulnerability exploitation possible at scale, raising the intrinsic cost or risk of the existence of individual vulnerabilities in these high-value targets. The question then becomes: what does this mean for the value of vulnerabilities in the global “marketplace,” and how might that change how vendors (and researchers) approach the security of these systems?
  • Decentralized finance (“DeFi”) coupling code and money more tightly than ever before, forcing those who believe “code is law” to reckon with the fact that all software has flaws. Large financial institutions each spend hundreds of millions – sometimes even billions – per year on cybersecurity, because they know that failing to do so may cost them even more. Are proportional investments being made in decentralized finance applications and cryptocurrencies? Not usually. While improvements in cyber resilience today are largely driven by regulatory interventions, in the sphere of decentralized finance (DeFi), where value is exclusively stored digitally and mediated directly by code, a threat actor could directly and immediately remove value from a company through attacking the underlying infrastructure, protocols, or cryptographic implementations. One leaked cryptographic key or a single software flaw could lead to the collapse of entire organizations.Time will tell whether we will see a bottom-up, market-driven push for higher assurance systems for serious decentralized finance companies, or whether they will become the highest-risk value stores on the planet.
  • As the COVID-19 pandemic rages on, how do we maintain privacy in a world in which we are accountable to one another? In our research this year, we conducted wide-ranging research into the security & privacy aspects of various jurisdictions’ vaccine passports, but other new forms of attestation and surveillance – including smart buildings, enhanced genomic and health testing, and mobile passports – remain under-studied by the security research community. Furthermore, the pandemic has breathed new life into large tech companies’ VR and augmented reality ambitions (“the metaverse”) which will affect not only the security and privacy of our devices and homes, but invites new kinds of sensors and interfaces (perhaps even brain-computer interfaces) which will be perhaps more intrinsically connected to our bodies, minds, and behaviour that anything we’ve seen before.
  • Finally, we must ask ourselves, what are the grand challenges for cybersecurity?What are the problems that matter the most to the security & privacy of individuals, organizations, and the internet? As an industry, we face a reckoning in which I believe that we need to elevate ourselves toward taking a more “scientific” and rigorous approach to the study of information security cause and effect, and let go of the unspoken agreements, copycat risk-mitigations, hearsay “best practices,” and other unacceptable industry norms. While the development of obscure and complex exploit chains will always have its place to enable us to understand the true frontiers of what we’re up against, it is time to prioritize the problems that most meaningfully affect the world in which we live. It is my intent that we become more methodical in our study of the mitigations of specific cybersecurity threats, to move beyond a world where companies sometimes feel they have no choice but to sink money into the same expensive and at times incomplete or misconfigured security products as their peers, without a real understanding of their effectiveness, and instead to a world where they are empowered through an understanding of the actual costs of both attack and defense.


Open Source Security Tool & Code Releases

In 2021, we released around 30 open source security tools, major tool updates, implementations, or other open-source repositories.

Among the security tools released by NCC Group this year are:

  • Covenant v0.7: Covenant is an open source .NET command and control framework that supports Red Team operations, similar in many ways to the well-known Cobalt Strike threat emulation software. NCC Group’s Simone Salucci and Daniel Lopez Jimenez contributed a number of features to the project that they desired while using it. Some of those features include: Disabling ETW, Dumping Snapshot Processes, Process Injection Tasks, Payload Execution Guardrails and Other Pull Requests
  • GTFOBLookup: An offline command line lookup utility for GTFOBins, LOLBAS, and WADComs, created by James Conlan.
  • KilledProcessCanary: A prototyped a Windows Service Canary in order to target parts of the ransomware kill chain to minimize impact and overall success of operations, created by Ollie Whitehouse. The tool was described in his blog post, Deception Engineering: exploring the use of Windows Service Canaries against ransomware
  • Libptmalloc: Heap analysis tooling for ptmalloc (pthreads malloc), and is interesting to those seeking to exploit glibc, created by Cedric Halbronn. This was part of the work discussed in the blog post, Exploiting the Sudo Baron Samedit vulnerability (CVE-2021-3156) on VMWare vCenter Server 7.0
  • Log4j-jndi-be-gone: A simple mitigation for CVE-2021-44228 (Log4Shell), created to help mitigate the log4j vulnerabilities which saw widespread exploitation in December 2021, by Jeff Dileo. This tool uses the Byte Buddy bytecode manipulation library to modify the at-issue log4j class’s method code and short circuit the JNDI interpolation handler. It works by effectively hooking the at-issue JndiLookup class’ lookup() method that Log4Shell exploits to load remote code, and forces it to stop early without actually loading the Log4Shell payload URL.  
  • ML-for-RNGs: The Jupyter notebooks underlying research exploring the utility of deep learning to predict the sequence of the (presumably) random output numbers using previously generated numbers without the knowledge of the seed for (non-cryptographic) PRNGs xorshift128 and Mersenne Twister, by Mostafa Hassan. While this research looked at a non-cryptographic PRNGs, we are interested, generically, in understanding how deep learning-based approaches to finding latent patterns within functions presumed to be generating random output could work, as a prerequisite to attempting to use deep learning to detect previously-unknown patterns in cryptographic (P)RNGs.
  • Ndsp-discover: An Nmap script to identify Netgear Switch Discovery Protocol (NSDP) on UDP ports 63322 and 63324, by Manuel Ginés Rodríguez. This tool was created in support of Manuel’s extensive vulnerability research on the Netgear ProSAFE Plus switches
  • NLAhoney: Source code to deploy honeypots that can capture RDP handshakes, then crack them offline in an effort to understand which passwords are being sprayed at RDP honeypots we deploy, created by Ollie Whitehouse and Ray Lai, as a part of their project, Cracking RDP NLA Supplied Credentials for Threat Intelligence
  • Principal Mapper (v1.1.0 Update; v1.1.4 Update): Principal Mapper, or PMapper, is a tool and library for in-depth analysis with AWS Identity and Access Management, as well as AWS Organizations. PMapper stores data about AWS accounts and organizations, then provides options to query, visualize, and analyze that data. The library is written in Python and was created by Erik Steringer. 
  • Raccoon: is a tool that aims to identify potential misconfigurations that could expose sensitive data within Salesforce. Specifically, it reveals where access has been granted to all records for particular objects of interest, by Jerome Smith.
  • Reliably-checked String Library Binding: is a library binding that uses static array extents to improve diagnostics that can help identify memory safety flaws, created by Robert Seacord. 
  • Ruby-trace: A Low-Level Tracer for Ruby, created by Jeff Dileo and originally released to coincide with his DEF CON 29 talk on it and parasitic tracing in general. Version 1.1.0 adds support for the newly released Ruby 3.1 and includes a number of improvements, including alternate enable/disable hooks.
  • Shouganaiyo-loader: A cross-platform Frida-based Node.js command-line tool that forces Java processes to load a Java/JVMTI agent regardless of whether or not the JVM has disabled the agent attach API, created by Jeff Dileo. 
  • Sigwhatever: For automated exploitation of netntlm hash capture via image tags in emails signatures. The tool was described in their blog post, Sign over Your Hashes – Stealing NetNTLM Hashes via Outlook Signatures created by David Cash, Rich Warren, Julian Storr.
  • SocksOverRDP: This tool adds the capability of a SOCKS proxy to Terminal Services (or Remote Desktop Services) and Citrix (XenApp/XenDesktop). It uses Dynamic Virtual Channel that enables us to communicate over an open RDP/Citrix connection without the need to open a new socket, connection or a port on a firewall, created by Balazs Bucsay.
  • Solitude: Solitude is an open source privacy analysis tool that enables you to conduct your own privacy investigations into where your private data goes once it leaves your web browser or mobile device. Whether a curious novice or a more advanced researcher, Solitude makes the process of evaluating an app’s privacy accessible for everyone, created by Dan Hastings. Solitude was featured in media outlets including KitPloit, Hacking Land, and Hacking Reviews.
  • Squeak: The tool was described in their blog post, MSSQL Lateral Movement, created by David Cash. This tool supports the work outlined within the blogpost: namely, the automation of lateral movement via MSSQL CLR without touching disk (besides a DLL being temporarily written to disk by the SQL Server process) or requiring XP_CMDSHELL.
  • UninstalledAppCanary: This project deploys a number of canary apps which fire when uninstalled, motivated by the idea that certain threat actors uninstall a number of products prior to dropping later stages, and was created by Ollie Whitehouse. The tool was described in his blog post, Deception Engineering: exploring the use of Windows Installer Packages against first stage payloads, and builds upon prior work discussed in the blog post Deception Engineering: exploring the use of Windows Service Canaries against ransomware
  • Wubes: is like Qubes (a security-focused operating system that aims to provide security through virtualization) but for Microsoft Windows. The purpose is to leverage the Windows Sandbox technology to spawn applications in isolation, so that if you browse a malicious site using Wubes, it won’t be able to infect your Windows host without additional chained exploits. Currently it supports spawning a Windows Sandbox for the Firefox browser but other applications can easily be added, and was created by Cedric Halbronn.

We also released source code for a variety of cryptographic implementations, exploit proofs-of-concept, and obfuscation reverse-engineering techniques:


Publicly Reported Security Audits

For many years, NCC Group has published publicly-reported security audits of critical components of open source software as well as select proprietary systems, including in past years for components of important systems including OpenSSL, SecureDrop, TrueCrypt, Tor, Docker, Keybase, Zcash, and many others. Of these reports, those labelled “Public Report” were developed as a part of a paid engagement with an NCC Group client for NCC Group to conduct and publish the findings of a security audit on in-scope components. In 2021, NCC Group delivered 8 Public Reports across a number of different cryptographic implementations, as well as for the Google One VPN and WhatApp End-to-End Encrypted Backups, among others.

Our 2021 Public Reports include: 


Applied Cryptography

  • In January 2021, Thomas Pornin published a blog post about his recent work on Double-odd Elliptic Curves. Double-odd curves are the elliptic curves whose order (number of points on the curve) is the double of an odd integer. About 1/4th of all curves are double-odd curves: this is a large class of curves, and Such curves have nominally been supported by generic standards such as ANSI X9.62 (ECDSA signatures over any curve) for the last two decades. He notes that the point of double-odd elliptic curves is to provide a safe structure, amenable to building cryptographic protocols on top of the “prime order group” abstraction, but that they also offer performance improvements over generic elliptic curves, making them especially useful for small embedded systems with severe constraints on power, RAM and CPU abilities. There is also a complete whitepaper published on the IACR ePrint archive, full of mathematical demonstrations; it also specifies the use of do255e and do255s in higher-level cryptographic functionalities (key pair generation, key exchange, signature, and hash-to-curve). Thomas has also released several cryptographic implementations, as well as a geometric introduction which helps build mathematical intuition related to double-odd curves. 
  • In January 2021, our Cryptography Services team (virtually) attended the Real-World Cryptography conference, and published a blog post in which they shared summaries and insights from some of our favourite talks from RWC 2021.
  • In January 2021, we published the NCC Group Cryptography Services Public Report on the BLST Cryptographic Implementation Review. This involved a cryptographic implementation review of the BLST library, which implements support for the draft IETF specifications on Hashing to Elliptic Curves and BLS Signatures. The latter specification uses advanced cryptographic-pairing operations to feature aggregation properties for secret keys, public keys and signatures, which is central to the emerging Ethereum 2.0 Proof-of-Stake block-validation mechanism. This report was commissioned by Supranational, Protocol Labs and the Ethereum Foundation. 
  • In January 2021, Gérald Doussot published Software Verification and Analysis Using Z3. This post provided a technical introduction on how to leverage the Z3 Theorem Prover to reason about the correctness of cryptographic software, protocols and otherwise, and to identify potential security vulnerabilities. In this post, he covered both: (1) Modeling and analysis of an algorithm documented in an old version of the QUIC Transport protocol IETF draft, as well as (2) Modeling of specific finite field arithmetic operations for elliptic curve cryptography, with integers represented using a uniform saturated limb schedule (four limbs of 64 bits), to prove equivalence with arbitrary-precision arithmetic, and for test case generation. In February, researchers from Galois published a blog post, “Cryptol as an SMT Frontend,” referencing Gérald’s research, in which they checked the implementation of part of the QUIC protocol, and built upon this work in June 2021, “Cryptographic Assurance with Cryptol,” which they explore an optimized implementation of field arithmetic.
  • In February 2021, Eli Sohl published Cryptopals: Exploiting CBC Padding Oracles. This post – which was the first in a new series by Eli which offers educational walkthroughs of the beloved Cryptopals cryptography challenges – explored the classic padding oracle attack on CBC-mode block ciphers, through the lens of Cryptopals challenge #17
  • In June 2021 we published the NCC Group Cryptography Services Public Report on the cryptography and implementation review of Protocol Labs Groth16 Proof Aggregation. This was a cryptography and implementation review of the Groth16 proof aggregation functionality in the bellperson and two other related GitHub repositories. This code utilizes inner product arguments to efficiently aggregate existing Groth16 proofs while reusing existing powers of tau ceremony transcripts. This report was commissioned by Protocol Labs. 
  • In June 2021, NCC Group’s Thomas Pornin – alongside external peers Liz Steininger, isis agora lovecruft, JP Aumasson, and Taylor Hornby – spoke on a panel on Auditing Cryptography at the Zcash Foundation’s conference, Zcon2lite.
  • In June 2021, Parnian Alimi published On the Use of Pedersen Commitments for Confidential Payments. This blog post looked at the Zether protocol, which uses ElGamal public key encryption to hide transaction amounts and utilizes zero-knowledge proofs to demonstrate the validity of a transaction to stakeholders in a financial blockchain, namely network validators, investors, and auditors. In general, this is important to the principle of transaction confidentiality, desirable for some financial blockchains, which requires hiding investors’ account balances and transaction amounts, while enforcing compliance rules and performing validity checks on all activities. The post also explains underlying cryptographic building blocks including Pedersen Commitment, ElGamal Encryption, and Zero-Knowledge Proofs, and also discusses a number of implementation considerations including privacy (vs confidentiality), the front-running problem, side-channel attacks, forward secrecy, and integrated signing & encryption. 
  • In June 2021, Eric Schorn published his first post in a two-part code-centric blog post series about selected optimizations found in pairing-based cryptography. For his first post, Optimizing Pairing-Based Cryptography: Montgomery Arithmetic in Rust, he discussed selected optimizations found in pairing-based cryptography, foundational to the BLS Signatures central to Ethereum 2.0, zero-knowledge arguments central to Zcash, Filecoin, and other blockchain/cryptocurrency projects relying upon zk-proofs. This post covered modular Montgomery arithmetic from start to finish, including context, alternatives, theory, and practical working code in Rust running 9X faster than a generic Big Integer implementation. His second post, published in September 2021, was about Optimizing Pairing-Based Cryptography: Montgomery Multiplication in Assembly. This second post takes the Montgomery multiplication algorithm developed in Rust even further to seek the maximum performance a modern x86-64 machine can deliver from an implementation hand-written in assembly language, resulting in a Montgomery multiplication routine running more than 15X faster than a generic Big Integer implementation, due to several specialized instructions and advanced micro-architectural features enabling increased parallelism.
  • In September 2021, Javed Samuel presented an Overview of Open Source Cryptography Vulnerabilities at the International Cryptographic Module Conference, ICMC21. This presentation reviewed the foundations of cryptographic vulnerabilities as applicable to open-source software from a penetration tester’s perspective over multiple public cryptography audit reports. This presentation also discussed what attacks in the past took advantage of these cryptography vulnerabilities and what the consequences were, as well as the success rate of various mitigations, as well as some thoughts on suggested focus areas for the future of open source cryptography. 
  • In October 2021, Thomas Pornin published Paradoxical Compression with Verifiable Delay Functions, which described (and implemented!) a mathematically-impossible compression algorithm, known as paradoxical compression, despite the mathematical impossibility of upholding three qualities (described in the paper/blog post) simultaneously. This is a good illustration of a fundamental concept of cryptography: namely that there is a great difference between knowing that some mathematical object exists, and being able to build it in practice.
  • In October 2021, we published the NCC Group Cryptography Services Public Report on their WhatsApp End-to-End Encrypted Backups Security Assessment. This report is the result of a security assessment we performed of its End-to-End Encrypted Backups project. End-to-End Encrypted Backups is an hardware security module (HSM) based key vault solution that aims to primarily support encrypted backup of WhatsApp user data. This report was commissioned by WhatsApp.
  • In November 2021, we published the Public Report – Zcash NU5 Cryptography Review. This report is the result of our review of the upcoming network protocol upgrade NU5 to the Zcash protocol (codenamed “Orchard”), and was commissioned by the Electric Coin Company.
  • In November 2021, Paul Bottinelli published Technical Advisory – Arbitrary Signature Forgery in Stark Bank ECDSA Libraries (CVE-2021-43572, CVE-2021-43570, CVE-2021-43569, CVE-2021-43568, CVE-2021-43571). These critical vulnerabilities allowed arbitrary signature forgery by an attacker, and existed in several open-source cryptography libraries – one with over 7.3M downloads in the previous 90 days on PyPI, and over 16,000 weekly downloads on npm.
  • In November 2021, Paul Bottinelli published An Illustrated Guide to Elliptic Curve Cryptography Validation. This blog post offered an illustrated description of the typical failures related to elliptic curve validation and how to avoid them in a clear and accessible way. This is important because Elliptic Curve Cryptography (ECC) is widely used to perform asymmetric cryptography operations, such as to establish shared secrets or for digital signatures, but insufficient validation of public keys and parameters is still a frequent cause of confusion, leading to serious vulnerabilities, such as leakage of secret keys, signature malleability or interoperability issues.
  • In November 2021, Frans van Dorsselaer of Fox IT (a part of NCC Group) presented at CWI Symposium on Post-Quantum Cryptography.
  • Based on the popularity of Eli Sohl’s original February 2021 blog post on the Matasano Cryptopals challenges, in December 2021 he began a blog series in which he offers walkthrough tutorial videos explaining the solutions to the Cryptopals challenges, with the post Announcing NCC Group’s Cryptopals Guided Tour.  
  • In December 2021, we published the NCC Group Cryptography Services Public Report – WhatsApp opaque-ke Cryptographic Implementation Review. In this work, we conducted a security assessment of the ‘opaque-ke’ library, an open source Rust implementation of the OPAQUE password authenticated key exchange protocol. The protocol is designed to allow password-based authentication in such a way that a server does not actually learn the plaintext value of the client’s password, only a blinded version of the password computed using a verifiable oblivious pseudorandom function. This report was commissioned by WhatsApp.
  • In December 2021, Jennifer Fernick presented Financial Post-Quantum Cryptography in Production: A CISO’s Guide for the Financial Services Information Sharing and Analysis Center, FS-ISAC. In this talk, starting from the known fact that most of the public-key cryptography on the internet will be trivially broken by existing quantum algorithms, she  covered strategic applied security topics to address this need for a cryptographic upgrade of the global financial cryptographic infrastructure, including: Financial services use cases for cryptography and quantum-resistance, and context-specific nuances in computing environments such as mainframes, HSMs, public cloud, CI/CD pipelines, customer-facing systems, third-party and multi-party financial protocols; Whether quantum technologies like QKD are necessary to achieve quantum-resistant security; Post-quantum cryptographic algorithms for digital signatures, key distribution, and encryption; How much confidence cryptanalysts currently have in the quantum-resistance of those ciphers, and what this may mean for cryptography standards over time, and designing extensible cryptographic architectures and deciding when to begin integrating PQC in a world of competing technology standards.


Attacking (& Attacking with) Machine Learning Systems

  • In June 2021, Jennifer Fernick published a blog post titled Machine Learning for Static Malware Analysis, with University College London. For this project, we sought to determine the efficacy of various individual machine learning primitives – as well as ensemble methods of multiple algorithms – for the static classification of Windows binaries in terms of whether or not they are malicious. The full research paper, written by CDT PhD students Emily Lewis, Toni Mlinarevic, and Alex Wilkinson of University College London, ultimately demonstrated that it is possible to create number of different high-efficacy machine learning models to identify malicious executables on the basis of features which included PE headers, bytes n-grams, control-flow graphs and API call graphs, all of which performed well. Making use of ensemble methods, the researchers were able to achieve a classification accuracy of 98.9%, suggesting that the particular featureset and ensemble model used (multi-modal late fusion) is effective for the detection of malware binaries at scale. Media coverage on this work, asking “Can machine learning help detect zero malware?” was published by The Cyber Post, SC Media, thisweekin4n6, and RedPacket Security
  • In June 2021, the NCC Group Data Science Team published Incremental Machine Learning by Example: Detecting Suspicious Activity with Zeek Data Streams, River, and JA3 Hashes. This post, which explores the incremental learning machine learning paradigm in a real-world setting, provides a simple example involving JA3 hashes showing how some of the foundational algorithms that enable incremental learning techniques can be applied to novelty detection (the first time something has happened) and outlier detection (rare events) on data streams derived from Zeek. This was also discussed in thisweekin4n6.
  • As a part of an August 2021 presentation at Black Hat USA on open source security, Jennifer Fernick discussed the impact that scalable, machine-learning driven vulnerability detection, triage, and potential exploitation methods could have on open-source targets in the coming years, arguing that scalable vulnerability finding methods are dual use, in that they can benefit either defenders or attackers, and the growth of these tools may exacerbate asymmetries between attack and defense.
  • In October 2021, Mostafa Hassan published a blog post series on Cracking Number Generators Using Machine Learning. In Part 1 of this series, he explored using deep learning to predict the sequence of the (presumably) random output numbers using previously generated numbers without the knowledge of the seed for the (non-cryptographic) PRNG, xorshift128, essentially to break the PRNG. In Part 2 of this series, he demonstrated the effectiveness of deep learning techniques against the so-called randomness of the (non-cryptographic) PRNG, Mersenne Twister. While this research looked at a non-cryptographic PRNGs, we are interested, generically, in understanding how deep learning-based approaches to finding latent patterns within functions presumed to be generating random output could work, as a prerequisite to attempting to use deep learning to find previously-unknown patterns in cryptographic (P)RNGs, as this could potentially serve as an interesting supplementary method for cryptanalysis of these functions. In these blog posts, we show our work in beginning to explore this space. This work was also discussed by Hackaday.
  • In December 2021, Margit Hazenbroek published Encryption Does Not Equal Invisibility – Detecting Anomalous TLS Certificates with the Half-Space-Trees Algorithm. This blog post outlined an approach to detecting suspicious TLS certificates using an incremental anomaly detection model. Specifically, this model utilized the Half-Space-Trees algorithm and provides NCC Group’s Managed Detection and Response Security Operations Center (SOC) with the opportunity to detect suspicious behavior, in real-time, even when network traffic is encrypted. 
  • In December 2021, NCC Group commenced our annual research project with the  Centre for Doctoral Training in Data Intensive Science (CDT in DIS) at University College London (UCL), where we will in 2022 be studying the use of Generative Adversarial Networks (GANs) for fuzzing.
  • In December 2021, Jennifer Fernick published On the malicious use of large language models like GPT-3, in which she asked,  “Can large language models generate exploits?” This blogpost explored the question of whether (and how) large language models like GPT-3 or their successors may be useful for exploit generation, and proposed an offensive security research agenda for large language models.

Into 2022, we will be continuing research on several A.I. security related projects including:

  • Real-world, practical attacks on machine learning systems, including both a whitepaper as well as new findings and attacks 
  • Machine learning techniques for vulnerability finding in C source code, studying the comparative performance between graph-based methods and NLP (natural language processing)-based methods
  • New work in the use of Generative Adversarial Networks (GANs) to improve fuzzer performance
  • Studying the security and privacy implications underlying chatbots, and studying an attack model that treats chatbots as an oracle for private or sensitive training data


Misinformation, Deepfakes, & Synthetic Media

  • In October 2021, Jennifer Fernick discussed the problem of deepfake detection in the Threatscape 2023 and Beyond panel at MapleSec, also covered by IT World Canada. In this presentation, she challenged the idea that deepfakes could ever be reliably detected, warning that the use of machine learning to both generate and detect deepfakes would result in a cyclical competition in which deepfake generation algorithms improve their output’s evasive capability against a given detection model, while detectors hone feature-detection, in a never-ending cycle. 
  • In November 2021, Swathi Nagarajan published Vaccine Misinformation Part 1: Misinformation Attacks as a Cyber Kill Chain. In this work, she used the Cyber Kill Chain model to describe vaccine misinformation attacks online, as well as to describe interventions against vaccine misinformation at each state of the killchain.

Into 2022, we will be continuing research that takes a security researcher’s toolkit and applies it to the problems of misinformation, deepfakes, and synthetic media, through ongoing projects including: 

  • Subverting facial recognition systems, including broadly-applicable, generic, “skeleton key” attacks on facial recognition systems (or as we’re calling them – “Eigenfaces”) 
  • Authenticating against deepfakes 
  • Understanding the offensive use of audio deepfakes in fraud, abuse, and account takeover
  • Experimental tooling for the automated debunking of misinformation 
  • Further security research and digital forensics methods for combating misinformation


Reducing Vulnerabilities at Scale & Improving Open Source Security

Software Supply Chain Security & Securing the Open Source Ecosystem

  • In February 2021, Jennifer Fernick of NCC Group (alongside colleagues from across OpenSSF) presented a panel at FOSS Backstage, titled Frontiers in Securing the Open Source Ecosystem. In this panel, the panelists explored challenges and opportunities in securing the open source software ecosystem against a range of threat actors through a variety of interventions at all phases of the software development lifecycle, giving a brief overview of the mission, priorities, and current work within the Open Source Security Foundation (openssf.org), including an end-to-end threat model of the open source ecosystem, and a discussion of some of the most pressing issues in the security of open source software.
  • In February 2021, Jennifer Fernick was interviewed by the GitHub README Project for an article about “How InfoSec pros keep open source safe—and how you can help”. 
  • In May 2021, the OpenSSF’s Identifying Security Threats Working Group announced their Security Metrics initiative. This working group, which began in mid-2020, was initially led by Jennifer Fernick of NCC Group and Michael Scovetta of Microsoft, aimed to create “dashboards” which enable developers and other users of open-source codebases to make security-informed decisions about the relatively security of their components, by providing both a graphical and API dashboard which summarizes key security metrics about a repo. Work in this group has also inspired and led to things like Google’s Security Scorecards this year, as well as the excellent Security Metrics project currently led by Scovetta.
  • In August 2021, Jennifer Fernick of NCC Group and Christopher Robinson of Intel presented Securing Open Source Software – End-to-End, at Massive Scale, Together at Black Hat USA. In this presentation, we shared key lessons learned in our experience coordinating the industry-wide remediation of some of the most impactful vulnerabilities ever disclosed (Heartbleed, Shellshock, Rowhammer, and BlueZ), presented a threat model of the many unmitigated challenges to securing the open source ecosystem, and shared new data which illustrates just how fragile and interdependent the security our core infrastructure can be, debate the challenges to securing OSS at scale. We also spoke oft-unspoken truths of coordinated disclosure and where it can fail, and discussed research advances that are making it easier for adversaries to find and exploit vulnerabilities at scale, offering guidance for how members of the security community can get involved and contribute meaningfully to improving the security of OSS – especially through coordinated industry-wide efforts. This work was covered by media outlets including Security Boulevard, the Veracode blog, Linux Today, SiliconANGLE, eSecurityPlanet, and Dark Reading.
  • Later that month, Jennifer was also voted onto the new Governing Board of the Open Source Security Foundation as the new General Member Representative, where joins senior technical executives from major technology companies including AWS, Cisco, Dell, GitHub, Google, Facebook, IBM, Intel, Microsoft, Oracle, Red Hat, VMWare, and more to help lead an industry-wide effort to help secure the open source ecosystem. This was discussed in detail in this press release
  • Since prior to OpenSSF’s founding in August 2020, Jennifer has been a founding member of the prior Governing Board as well as the Technical Advisory Committee, and made considerable contributions in the first several months of 2021 to help drive improved decision-making and governance within OpenSSF, to help advise senior US Congressional staffers on supply chain security, and to contribute to the technical vision for OpenSSF. She has also begun to advocate for the creation of a security incident response helpline for under-resourced open source projects who believe their project to be under active attack and require emergency intervention.
  • In the wake of the Log4Shell vulnerability (CVE-2021-44228, as well as CVE-2021-45046 and CVE-2021-44832) affecting the open-source project, Log4J, we responded in a number of ways: Firstly, by publishing our threat intelligence blog post, Log4Shell: Reconnaissance and post exploitation network detection which was frequently updated between December 12th and 29th to include Suricata network detection rules that can be used not only to detect exploitation attempts, but also indications of successful exploitation, as well as a list of indicators of compromise, relevant pcaps, and a number of references for prevention and mitigation. Secondly, Jeff Dileo released log4j-jndi-be-gone: A simple mitigation for CVE-2021-44228, which can be used to stop log4j from loading classes remotely over LDAP, preventing malicious inputs from triggering the “Log4Shell” vulnerability and gaining remote code execution on affected systems. We also maintained a highly-active meta thread on Reddit on the log4j 0day being exploited covering the unfolding events of Log4J, discussing advice, detection, response and remediation. Our hot patch, as well as our threat intelligence were cited in the US Cybersecurity & Infrastructure Security Agency’s Alert, “Mitigating Log4Shell and Other Log4j-Related Vulnerabilities” on December 22 2021. 


Secure Programming 

  • In March 2021, Robert Seacord (in collaboration with Jens Gustedt of INRIA) presented their paper on C language mechanism for error handling and deferred cleanup at the ACM/SIGAPP Symposium on Applied Computing. This paper introduces the implementation of a C language mechanism for error handling and deferred cleanup adapted from similar features in the Go programming language, which improves the proximity, visibility, maintainability, robustness, and security of cleanup and error handling over existing language features. This feature is currently under consideration for inclusion for forthcoming versions of the C programming language standard. A reference implementation of the features described by this paper is also available under an open source (BSD) license. This paper was also published in the conference’s proceedings, SAC ’21: Proceedings of the 36th Annual ACM Symposium on Applied Computing
  • In March 2021, Robert Seacord published a recording of his internal conference presentation, The Future of C Code Review, in which he discussed optimizations resulting from pointer provenance-based alias analysis in the C programming language that can modify the behavior of code with undefined behaviors, ultimately explaining what pointer provenance is, how it can introduce security flaws into your C program, and how to spot the problem and repair it. This presentation was based upon his Draft Technical Specification, “A Provenance-aware Memory Object Model for C,” submitted to ISO TC 1/SC22/WG14, the C Standards Committee, in September 2020.
  • In May 2021, Robert Seacord published a blog post in which he argues that supply chain security begins with secure software development.
  • In July 2021, Robert Seacord released Reliably-checked String Library Binding, a library binding that uses static array extents to improve diagnostics that can help identify memory safety flaws. This tool is part of his work on broader initiatives in the C Standards Committee to improve bounds checking for array types. 
  • In August 2021, Robert Seacord presented his training, Secure Coding in C, at Black Hat USA, which offered detailed explanation of common programming errors in C and C++ and describes how these errors can lead to code that is vulnerable to exploitation, and demonstrates specific remediation techniques as well as general secure coding practices that help prevent the introduction of vulnerabilities in C and C++ codebases. 
  • In September 2021, Robert Seacord presented Why Can’t Johnny Code Securely? at CyberEd 2021. ​​This presentation discussed how software developer demand continues to increase faster than universities can produce qualified graduates, increasing the software development skills gap, and argued that graduates are inadequately prepared to write secure code, which is a problem because inadequately prepared programmers tend to experiment randomly until they find a combination that appears to work but produces code that works only under optimal conditions but is insecure. This talk explored this problem and offered solutions toward developing a more efficient, effective, and skilled software developer workforce, more capable of writing secure code. 
  • In September 2021, Robert Seacord presented Secure Coding at Auto-ISAC Analysts.
  • In October 2021, Robert Seacord submitted 5 papers to the C Standards Committee (ISO/IEC WG14), including: 
  • He explained what each of these meant for the future security of the C programming language in his blog post, The Next C Language Standard, C23
  • In October 2021, Robert Seacord presented Volatility Ahead at NDC TechTown 2021. which explained – in the context of the C programming language – where volatile is useful, what the C and C++ standards say and how they got there, and finally suggest how the standards might be revised in the future. He also presented Secure Coding in C and C++, a two day training course that provides a detailed explanation of common programming errors in C and C++ and describes how these errors can lead to code that is vulnerable to exploitation.


Standardization & Verification 

In addition to the C Standards work outlined above, NCC Group made standards-related contributions in the following ways:

  • In February 2021, Matt Lewis released his paper, Investigating Potential Security Vulnerability Manifestation through Various Analyses & Inferences Regarding Internet RFCs (and how RFC Security might be Improved), to help understand how and why security vulnerabilities manifest, from design to implementation. This research parsed IETF RFCs, extracting RFC data and metadata into graph databases to explore and query relationships between different properties of RFCs. The ultimate aim of this work was to use any key observations and insights to stimulate further thought and discussion on how and where security improvements could be made to the RFC process, allowing for maximised security assurance at protocol specification and design so as to facilitate security and defence-in-depth. The research showed the value of mining large volumes of data for the purpose of gaining useful insights, and the value of techniques such as graph databases to help cut through the complexities involved with processing and interpreting large volumes of data.
  • In July 2021, Ollie Whitehouse (alongside external collaborators Kirsty Paine of the UK National Cyber Security Centre and James Sellwood of Twilio) published the IETF Draft, Indicators of Compromise (IoCs) and Their Role in Attack Defence. This draft reviews the fundamentals, opportunities, operational limitations, and best practices for the use of Indicators of Compromise (IoCs) to identify, trace, and block malicious activity in networks or on endpoints. It highlights the need for IoCs to be detectable in implementations of Internet protocols, tools, and technologies – both for the IoCs’ initial discovery and their use in detection – and provides a foundation for new approaches to operational challenges in network security. This work was presented at IETF 111, the presentation for which can be viewed here
  • In September 2021, Matt Lewis presented at the Safety, Security & Verification in Critical Systems Conference in Manchester. 
  • In November 2021, Matt Lewis of NCC Group alongside Mark McFadden of Internet Policy Advisors LTD (an expert on the development of global internet addressing standards and policies, and an active contributor to work in the IETF and ICANN), presented at the IETF Internet Architecture Board Workshop on Analyzing IETF Data. In this presentation, they sought to baseline how RFC Security Considerations – including RFC3552 (Guidelines for Writing RC Text on Security Considerations) – should be expressed and improved, seeking to use improvements to the RFC process itself to improve the security of the resulting standards. 

Into 2022, we will be continuing research on reducing vulnerabilities at scale through ongoing yet-unpublished projects such as:

  • Rewriting BSD kernel modules in memory-safe languages
  • Continued contributions to the work of the Open Source Security Foundation, both technically and from a leadership perspective, including toward improving coordinated vulnerability disclosure and incident response for open source projects
  • New contributions to programming language standards and internet architecture specs  
  • Creating an “entomology” of security bug types observed in Rust
  • Continued experimentation with vulnerability-finding query languages, including CodeQL
  • A whitepaper on Automated Exploit Generation (AEG)


Virtualization, Emulation, & Containerization

  • In January 2021, Jeff Dileo released proof-of-concept exploit code for his vulnerability, CVE-2020-15257, found in containerd – a container runtime underpinning Docker and common Kubernetes configurations – which resulted in full root container escape for a common container configuration. This was a flaw we disclosed in late 2020 for which a technical deep-dive is available in a blog post entitled ABSTRACT SHIMMER (CVE-2020-15257): Host Networking is root-Equivalent, Again
  • In May 2021, Jeff Dileo and ex-NCC Group colleague Addison Amiri presented their research on dRuby Security Internals at NorthSec. This presentation discussed how dRuby (a “distributed object system” built into Ruby – think CORBA or Java’s RMI) works, where its insecurities lie, and how it is much more insecure than previously understood to be, “which is a feat, considering that dRuby already provides code execution as a service.” In this presentation they discussed the dRuby API, its internals, and its underlying wire protocol — and how they make dRuby fundamentally unsafe — and demonstrated several novel proof-of-concept exploits targeting dRuby servers and clients, the latter of which have not been known to be vulnerable until this research was conducted. While dRuby is arguably well-known to be a readily exploitable service enabling remote code execution, this research shows how the underlying protocol exposes a number of additional risks that enable not only alternate methods of compromising dRuby services, but also the means to compromise dRuby clients. They also critiqued some of the existing advice and documentation for “securing” dRuby and how it fails to guard against dRuby’s inherent issues, as well as the researchers’ own efforts to harden dRuby, including the kinds of protocol, logic, and API changes needed to negate its issues. This presentation closed with a discussion of the insecurity of existing dRuby exploits, and showed how you can penalize your pentesters for using off-the-shelf exploits, including that Metasploit’s exploit for dRuby used the standard dRuby library to exploit dRuby, thus making it vulnerable to both standard and novel dRuby exploitation itself. A recording of this presentation is also available here
  • Also at NorthSec, Jeff Dileo joined a number of other researchers on a panel on Vulnerability Research. 
  • Throughout the year, Brian Hong presented Sleight of ARM: Demystifying Intel Houdini at a variety of conferences including Ekoparty, DEF CON 29 (Main Track), ToorCon and the Black Hat USA Briefings. This research targeted Intel’s proprietary Houdini binary translator, which runs ARM binaries on x86 platforms, such as higher-end Chromebooks and desktop Android emulators. This presentation began with a high-level discussion of what we uncovered about how Houdini works and is loaded into processes, then dived into the low-level internals of the Houdini engine and memory model, including several security weaknesses it introduces into processes using it. Brian concluded by discussing methods to escape the Houdini environment, execute arbitrary ARM and x86, and write Houdini-targeted malware that bypasses existing platform analysis.
  • Iain Smart also taught the training Mastering Container Security V5 – Black Hat edition at Black Hat USA. The course covered Docker and how Linux containers work, fundamental Linux security concepts, and container orchestration and clustering, looking at how Kubernetes works and the security pitfalls that can leave the clusters and cloud-based environments which use containers exposed to attack, as well as practical examples of configuring key Kubernetes security controls such as RBAC, PodSecurityPolicies and Network Policies.
  • At DEF CON 29, Jeff Dileo presented Instrument and Find Out: Writing Parasitic Tracers for High(-Level) Languages. This presentation discussed the process for developing generalized parasitic tracers targeting specific programming languages and runtimes using Ruby as our case study, showing the feasibility of writing external tracers targeting a language and its runtime, challenging the notion (and quality) of the performance monitoring and introspectability features of (some) modern programming languages by writing his own implementation and instrumenting them into the language dynamically.
  • In August 2021, Jeff Dileo also published Some Musings on Common (eBPF) Linux Tracing Bugs, which discussed an insecure coding pattern commonly used in system observability and program analysis tools, and several techniques that can enable one to evade observation from such tools using that pattern, especially when they are being used for security event analysis. 
  • In August 2021, Iain Smart was interviewed on how container vulnerabilities can put the software supply chain at risk, discussing how compromised external dependencies can enable an attacker to access and even potentially modify a build process.
  • In September 2021, Iain Smart published a blog post titled NSA & CISA Kubernetes Security Guidance – A Critical Review, in which he critiqued the recent Cybersecurity Technical Report (CTR) on Kubernetes Hardening Guidance, authored by the United States’ National Security Agency (NSA) and Cybersecurity and Infrastructure Security Agency (CISA). In this post, he gave an overview of the guidance, and highlighted specific places where the NSA/CISA guidance overlooked important aspects of Kubernetes security, or where the guidance was out of date at time of publication, as well as offering considerations for some of the more common complex use cases not covered by the CTR guidance, including useful audit configurations that won’t require excessive levels of compute power or storage, advice on handling external dependencies, and some notes around the complex mechanisms of Kubernetes RBAC.
  • In October 2021, Jeff Dileo discussed container security on the Cyberwire podcast
  • In November 2021, Ben Lister and Kane Ryans published Detection Engineering for Kubernetes clusters. This blog post detailed the collaboration between NCC Group’s Detection Engineering team and Containerization & Orchestration practice in tackling detection engineering for Kubernetes, including both a description of the detection engineering team’s more generic methodology around detection engineering for new/emerging technologies and how it was used when developing detections for Kubernetes-based attacks, as well as detailing the novel detection rules they have created around how privilege escalation is achieved within a Kubernetes cluster, to better enable security operations teams to monitor security-related events on Kubernetes clusters and thus to help defend them in real-world use.

Into 2022, we will be continuing research on virtualization, emulation, and container security, including but not limited to:

  • Container tracing and other research pertaining to container runtimes 
  • Transient execution vulnerabilities
  • Security audits and associated research related to key open source projects in this space


Hardware & Embedded Systems

  • In March 2021, Eric Evenchick offered the training Reverse Engineering Firmware with Ghidra at Black Hat Spring Trainings. This hands-on course taught the concepts, tools, and techniques required to reverse engineer firmware and assess embedded devices, using Ghidra, a popular, extensible, and powerful open-source reverse engineering tool that supports many different processor architectures, developed by the National Security Agency. In this training, participants used binary reverse engineering techniques to find exploitable vulnerabilities in an embedded Linux device, mapped device vector tables, peripheral memory, and system calls to find exploitable vulnerabilities in a bare-metal device, and identified remotely exploitable vulnerabilities in a Bluetooth Low Energy device. In July 2021, this training was also offered at Hardwear.io USA
  • In May 2021, we published our Public Report – Dell Secured Component Verification, which was commissioned by Dell to explore supply chain security functionality and related and supportive foundational security functionality on 14th and 15th generation Dell servers.
  • In May 2021, Eric Evenchick presented Building CANtact Pro: An Open Source CAN Bus Tool at NorthSec.  In this talk, he discussed the design and release process for the CANtact Pro device, to help would-be tool creators interested in launching their own hardware product to understand the many things that go into bringing a hardware idea to market, from PCB design to driver development. He also discussed open source tools for designing PCBs, writing cross-platform drivers using Rust, the economics of releasing a device, and logistical challenges one can expect when building hardware. 
  • In July 2021, Sultan Qasim Khan and Jeremy Boone published a three-part blog series on Alternative Approaches for Fault Injection Countermeasures. In Part 1 of this series, they covered the basic principles of fault injection – types of glitches, their effects, and how an attacker can characterize hardware and firmware to achieve a successful glitch. In Part 2 of this series, they discussed various C functions, macros and programming patterns that can be used to achieve double glitch resistance within software. In Part 3 of this series, they enumerated the drawbacks of common software-based glitching countermeasures, and outlined alternative countermeasures to fault injection, including instruction duplication, memory store verification, and some forms of control flow integrity intended to provide fault detection. 
  • In July 2021, Rob Wood published Practical Considerations of Right-to-Repair Legislation. In this blog post, he offered OEM, device owner, and US Federal legislators’ perspectives on right-to-repair, and discussed how device security requires trade-offs and compromises with usability, performance, cost, and repairability, and outlined specific implications of the proposed US legislation and how an OEM might alter their designs to comply.
  • In August 2021, Jon Szymaniak presented Depthcharge: A Framework for U-Boot Hacking at DEF CON 29 Demo Labs. Depthcharge is an extensible Python 3 toolkit designed to aid security researchers when analyzing a customized, product-specific build of the U-Boot bootloader. Depthcharge was first released at a Hardwear.io session in 2020, and also presented at the Open Source Firmware Conference, in a talk titled, Guiding Engineering Teams Toward a More Secure Usage of U-Boot.
  • In August 2021, Diana Dragusin published, The ABCs of NFC chip security, which explored how the complexity of the NFC technology and standardization ecosystem is contributing to security weaknesses. She also surveyed a range of NFC chips by a wide and representative range of vendors, and published findings in terms of things like user memory protections and system configuration protections, and demonstrating the importance of product threat-modelling when selecting which NFC chips to use in new hardware products.  
  • In December 2021, Catalin Visinescu published, Why IoT Security Matters, in which he makes a case for why IoT security matters, and walks us through a threat modelling exercise to understand the different ways internet-connected devices can be attacked. 
  • Later that month, Catalin Visinescu also published Choosing the Right MCU for Your Embedded Device — Desired Security Features of Microcontrollers. This article illuminated important security criteria that must be evaluated when choosing the right MCU component for an embedded systems project, helping to establish the questions engineers should ask chip vendors before deciding what is the best microcontroller for their new product.
  • In December 2021, Simon Watson published FPGAs: Security Through Obscurity?, which discussed emerging use cases of FPGAs, the changing FPGA technology landscape & how it affects security, recent FPGA vulnerabilities (including Starbleed, Thangrycat, JackHammer, & others), as well as attacks & defenses for FPGAs.


5G Security & Smart Environments

  • In June 2021, Traficom – the Finnish transport and communications agency – along with the Aalto University, Cisco, Ericsson, Nokia, and PwC, organized the 5G Cyber Security Hack competition, and annual hackathon-style event in which around 130 security experts from around the world participated in hacking challenges relating to 5G technology and its use cases. For the 2021 competition, NCC Group won first-place in their challenge, through the talented efforts of Ross Bradley, Eva Esteban Molina, Phil Marsden and Mark Tedman.


  • In October 2021, Mark Tedman published The Challenges of Fuzzing 5G Protocols. In this post, he discusses the specific challenges unique to fuzzing 5G telecommunications protocols using both proprietary and open source fuzzers (including Fuzzowski 5GC, Frizzer, and AFLNet), as well as the relative strengths and weaknesses of the fuzzers studied for their efficacy at 5G protocol fuzzing of the NGAP, GTPU, PFCP, and DIAMETER protocols. It also discussed details of the testing environment, a sample of the vulnerabilities found, the comparative performance of the tested fuzzers, as well as lessons learned for telecommunications protocol fuzzing in general.
  • In November 2021, Philip Marsden presented at Cyber Senate Control Systems Cybersecurity Europe Conference on the 5G Threat Landscape, in which he discussed a range of threats to 5G security, including those relating to systems architecture, policy, standards, and the security of the hardware and software supply chain. 
  • Throughout 2021 and into 2022, Daniel Romero and colleagues have been studying implementations of the  LoRaWAN protocol, initially with the intention to understand & improve the security testing of LoRaWAN networks. However, as the project progressed, it has expanded to become both a rigorous study of the LoRaWAN protocol itself, as well as an end-to-end study of the LoRaWAN network environment, from end-devices to final applications. In addition to creating security testing methodologies, one of the main outcomes this research has provided is the FLoRa framework and associated tooling. We have developed a modular tool which allows testing of the security of full LoRaWAN network implementations, and that has recently been updated with novel protocol vulnerabilities identified during a joint research with an academic research partner, to be published in 2022. 
  • Another ongoing project involves research into the security of the 4G/LTE Control Plane. During this research, several vulnerabilities that affect the LTE protocol and its implementation are being studied, as is the impact of their exploitation.

Into 2022, we will be continuing research on various aspect of 4G/LTE and 5G security, as well as protocols including LoRaWAN, and a range of research projects on smart environments, including:

  • Continued work on LoRaWAN tooling & security testing, including collaborative work with the University of Surrey
  • Vulnerability assessments of IoT components of smart environments, including smart locks and smart alarm systems, as well as smart buildings as a whole
  • Continued 4G/LTE control plane research
  • 5G Baseband SoC Research


Public Interest Technology

In NCC Group’s Research division, we have a dedicated research working group which offers paid research time and other resources to research projects conducted in the public interest, in an effort to support security and privacy research for the greater good of society which might not otherwise have resources available to support it. It is my hope that this group will continue to grow with each passing year.  

This year, our researchers focussed on a deep and broad analysis of the security & privacy implications of different vaccine passport apps around the world, the important topic of racial injustice in algorithmic decision-making, and mobile privacy from the perspectives of both users seeking to understand private data leakage from their favourite apps, as well as the true privacy impact of mobile-device-management (MDM) apps that employees are often asked to use in a bring-your-own-device (BYOD) scenario. We also continued our longstanding research partnership with independent UK consumer body Which?, where we studied a number of consumer IoT devices and reported on their security and privacy flaws to help consumers make informed choices about the devices they allow into their lives.

Vaccine Passport Security

  • In October 2021, Sid Adukia published Assessing the security and privacy of Vaccine Passports. In this blog post, he offered a framework of security and privacy considerations for vaccine credential systems, through threat modelling the different ways that they could be subverted, especially in ways that are harmful to users. 
  • This work was also complimented by Sid’s publication of Technical Advisory – New York State Excelsior Pass Vaccine Passport Credential Forgery which showed that an individual would be able to to create and store fake vaccine credentials in their NYS Excelsior Pass Wallet that might allow them to gain access to physical spaces (such as businesses and event venues) where they would not be allowed without a vaccine credential, even when they have not received a COVID-19 vaccine. The impact of this was vast, affecting all whose safety in physical event spaces in New York State depended upon the validity of people’s proof-of-vaccination, in a state with a population of over 20 million people.
  • Around the same time, Dan Hastings published Technical Advisory – New York State Excelsior Pass Vaccine Passport Scanner App Sends Data to a Third Party not Specified in Privacy Policy. The New York State (NYS) Excelsior Scanner App is used by businesses or event venues to scan the QR codes contained in the NYS Excelsior wallet app to verify that an individual has either a negative COVID-19 test or their vaccination status. In this work, Dan showed that some data about the businesses/event venues using the app to scan QR codes is also sent to a third-party analytics domain, but that this was not specified in the app’s privacy policy.
  • Combined, Sid and Dan’s work on the security & privacy impacts of some of the early vaccine credential systems received ample media coverage, including in a podcast interview with Security Ledger, as part of a story on Vox Recode and in POLITICO, and the forged credential vulnerabilities were covered in 18+ global publications including ZDNet, InfoRiskToday, Fuentitech, and DataBreachToday Europe.
  • In December 2021, Drew Wade, Emily Liu, and Sid Adukia published Exploring the Security & Privacy of Canada’s Digital Proof of Vaccination Programs. In this work, they studied a range of Canadian provinces’ proof-of-vaccination apps to analyze their associated security and privacy properties. They also offered a deep-dive exploration of the Verify Ontario app in depth to understand its ability to correctly reject forged and malformed vaccine credentials, to avoid collecting unnecessary private data, and to manage data using security best-practices.


Algorithmic Bias & Racial Injustice 

  • In August 2021, Tennisha Martin presented How Bias and Discrimination will have members of the Black community Incarcerated or Dead at the DEF CON 29 Blacks In Cyber Village. This presentation focussed on the use of machine learning and other automated/algorithmic systems in the healthcare and criminal justice settings, and how the aggregation of data and the formulation of algorithms by a largely homogeneous population results in bias and discrimination against people of colour. She highlighted examples of real-world harms including the racial impact of predictive policing on the jail times served by people of different races, and the entrenched bias in medical diagnostic algorithms that more often fails to diagnose and treat certain illnesses in patients of colour, resulting in poorer health outcomes for this group. Overall, she shared the message that the bias and discrimination in many application areas of artificial intelligence will have members of the Black community incarcerated or dead, and that these systems must be interrogated and improved to create a more just and equitable world for all. The video recording of this presentation is available here


Mobile Privacy for Every User 

  • In March 2021, Dan Hastings and Emanuel Flores officially released Solitude, an open-source privacy analysis tool. Solitude aims to help people inspect where their private data goes once it leaves their favorite mobile or web applications on iOS or Android. Whether a curious novice or a more advanced researcher, Solitude makes the process of evaluating an app’s privacy accessible for everyone. Since privacy policies are often difficult to understand when trying to identify how your private data is being shared and whom it’s being shared with – and privacy policies don’t always tell the complete truth of what an app’s actual data collection practices are – Solitude was built to help give more transparency to users of where their private data goes. Solitude makes the process of proxying HTTP traffic and searching through HTTP traffic more straightforward. Solitude can be configured to look for any type of data that you input in a mobile or web application and reveal where that data is going. The application inspects all outbound HTTP traffic, looks for various hashes of your data and recursively decodes common encoding schemes (base64,URL). In August 2021, Dan presented on Solitude at the Black Hat USA Arsenal, as well as at the USENIX Symposium on Usable Privacy and Security (SOUPS). Demos of this tool have also previously been presented at Chaos Communication Congress, and it has received media coverage by Hackin9, as well as been used in journalistic investigations conducted by reporters from a major U.S. daily paper. This work also resulted thus far in two technical advisories associated with pasteboard data leakage vulnerabilities:
  • In June 2021, Nick Galloway published iOS User Enrollment and Trusted Certificates. This research involved a study of MDM (mobile device management) on iOS 13, specifically finding that the User Enrollment MDM option added with iOS 13 does not restrict MDM-deployed certificates to MDM-deployed applications. In practice, what this means is that personally installed (ie: non-work) apps will trust MDM-deployed (ie: employer) certificates, in the absence of additional controls such as certificate pinning. When using User Enrollment on the organization’s Wi-Fi, it is possible for a corporate Intrusion Detection System to collect personal data by monitoring intercepted traffic, seriously compromising an individual’s privacy on their own devices by virtue of trusting their employer’s certificates for all activity on the device.


Defending Good-Faith Security Research

  • In June 2021, NCC Group also co-signed the Electronic Frontier Foundation’s Statement on DMCA Use Against Security Researchers, alongside over a dozen other security firms. This statement voiced opposition to the use of Section 1201 of the Digital Millennium Copyright Act against security researchers performing research in good faith, including when using third-party security testing tools, where we stated, “We believe that the security of the internet and our digital world is strengthened by the work of independent security researchers who seek to discover and remediate existing security vulnerabilities before those vulnerabilities can be uncovered and exploited by threat actors, and we urge reconsideration of policy which may inhibit this important work.”



Consumer IoT Device Security

  • Throughout the year, in an ongoing campaign to improve the security of consumer-oriented IoT devices in collaboration with independent UK consumer body Which?, Guy Morley and Dale Pavey conducted four research projects related to vulnerabilities and risks associated with different classes of IoT devices including a range of smart home devices, connected dash cams, and e-Scooters:
    • In their research into the security of smart homes, conducted in May 2021 in collaboration with the Global Cyber Alliance (GCA) and reported on by Which? in July 2021, How a smart home could be at risk from hackers, Guy and Dale built a smart home honeypot – including a collection of smart TVs, printers and wireless security cameras and Wi-Fi kettles – and detected more than 12,000 scanning or hacking attempts in a single week. They put these findings in context, offering an explanation as to why these attacks occur, and offering actionable advice for users to help better secure their smart homes. This was further discussed in their blog post, Honeypot research reveals the connected life might not be so sweet, where they highlighted both the user risk (such as leaked smart camera streams) of attacks on smart home devices, as well as threat actors’ broader desire to use the compromised devices themselves to construct botnets to perform wider, more powerful hacking attempts, helping general users of smart home devices to better understand this well-understood phenomenon. 
    • In their research into broad, ecosystem-wide vulnerabilities in the IoT and app ecosystem reported on by Which? in November 2021, Online marketplaces flooded with insecure smart products, Guy and Dale studied more than 1,800 individual smart products listed on UK online marketplaces including Amazon, eBay and AliExpress, including smart doorbells, wireless cameras and tablets. To conduct this research, they look at a range of generic and clone smart products and trawled online marketplaces for key words associated with these products. Interestingly, 1,727 of the products found used just four apps – Aiwit, CamHi, CloudEdge and Smart Life – where a number of security issues were identified. Issues including weak password security, unencrypted data transfer, vague privacy policies, lack of means for coordinated disclosure of vulnerabilities with device manufacturers, and some devices being out of support for more than 7 years. Unfortunately, these weren’t low-hanging fruit, either: The identified devices had 37,129 reviews on Amazon at an average 4.1 star rating, and 15 of them featured Amazon Choice labels. This work was further discussed in their blog post, Home is where the hack is.
    • Based on the research findings above, Which also produced a video, It’s this EASY to hack your smart home, in which Guy Morley joins Which? to demonstrate attacks on a smart doorbell, connected camera, printer, smart lock, and more, demonstrating how one compromised device can be used to more easily compromise other devices – in this case, unlocking the home’s door via its’ insecure smart lock.
    • In their dashcam research reported on by Which? in July 2021, How secure is the data on your dash cam? Guy and Dale studied the security of 9 dash cams – from BlackVue, Garmin, Halfords, Kitvision, MiVue, Nextbase, Road Angel, Transcend and Viofo – resulting in pervasive findings of weak encryption, lack of server protection, and weak passwords, noting that, “If a criminal were able to access the data on a dash cam, they could use it to work out where you live, where you work, what time you usually leave the house and where you go – not to mention being able to delete something incriminating from your recordings.” This was further discussed in their blog post, Are dash cam users en-route to security risks?, which also explored the ongoing debate about whether GPS and dash cam data should be considered personal data. 
    • In their recent research into e-Scooters, Guy and Dale looked at the security and safety of 10 popular brands s-Scooters, and found a number of safety issues in relation to electronic brakes and the ability to remotely tamper with the devices via Bluetooth-based attacks. They also found that a number of them could be modified quite easily to push them beyond legal speed limits. At time of writing, this work is undergoing coordinated disclosure with the affected manufacturers, for publication of findings set for early 2022. 



Cloud & CI/CD Pipeline Security

  • This year, Viktor Gazdag joined to the Center for Internet Security community and made technical contributions to the CIS Benchmarks for Securing Microsoft 365. The benchmark has two levels of checks and recommendations that provides guidance for creating and configuring a baseline security in their Microsoft 365 tenant. At time of writing, the CIS Microsoft 365 Foundations Benchmark was in version 1.3.0, to which Viktor has contributed.
  • In March 2021, Erik Steringer published version 1.1.0 of his tool, Principal Mapper (PMapper), a tool for quickly evaluating IAM permissions in AWS. 
  • In May 2021, Xavier Garceau-Aranda gave a training at NorthSec (which was offered again by NCC Group in August at Black Hat USA 2021) titled Offensive Cloud Security. This training allowed ​​attendees to experience first-hand how security vectors that exist in ecosystems where conventional technologies integrate with cloud-based solutions present opportunities for abuse by attackers, as well as the detection and mitigation of these attacks. The training was structured as a sequence of scenarios, which mix theory and practical exercises in multi-cloud environments, exploring topics like leveraging CI/CD systems to gain a foothold into cloud environments, lateral movement & privilege escalation, abusing containers & clusters, hybrid networks & moving from the management plane to the resources plane, Azure AD synchronization mechanisms and pitfalls, and more. Ultimately, while this training took an offensive perspective on cloud security, it aimed to show how to defend and mitigate against a range of attacks against public and hybrid cloud systems. 
  • In June 2021, Jerome Smith published a blog post titled “Are you oversharing (in Salesforce)? Our new tool could sniff it out!” This post introduces Jerome’s new open-source tool, Raccoon, which aims to identify potential misconfigurations that could expose sensitive data within Salesforce, revealing where access has been granted to all records for particular objects of interest. This helps combat the “complex relationship between role hierarchies, user permissions, sharing rules, and exceptions for certain situations” within Salesforce, helping to mitigate the common concern about potential unauthorized access to data among clients who commission security assessments on their instances of Salesforce. 
  • In August 2021, Tim Rawlins discussed the technical debt incurred by a rapid shift to cloud to support remote working during the COVID-19 pandemic with Communications of the ACM
  • In August 2021,  Erik Steringer presented his open source tool, Principal Mapper (PMapper), at Black Hat USA Arsenal and DEF CON 29 Demo Labs. Principal Mapper is a script and library for identifying risks in the configuration of AWS Identity and Access Management (IAM) for an AWS account or an AWS organization. It models the different IAM Users and Roles in an account as a directed graph, which enables checks for privilege escalation and for alternate paths an attacker could take to gain access to a resource or action in AWS. 
  • In September 2021, Erik Steringer presented Automating AWS Privilege Escalation Risk Detection With Principal Mapper at fwd:cloudsec at the Marriott City Center in Salt Lake City, in which he discussed how to use his open source tool, Principal Mapper (PMapper), for in-depth evaluation of AWS IAM Authorization Risks, as well as how to extend it to automate finding risks (continuous monitoring) and test for resource isolation. The video of this presentation is available here
  • In September 2021, Dirk-Jan Mollema presented Breaking Azure AD joined endpoints in zero-trust environments at RomHack 2021. In this presentation, he asked, “how much trust is zero trust anyway?” and discussed how as more security controls are added to protect cloud accounts, much of that trust is concentrated at a user’s endpoint, where long-term credentials are stored which comply with strict security policies, such as the use of 2FA. However, he noted that while hardware protection and use of Trusted Platform Modules (TPMs) would ideally offer high assurance of these credentials’ security, that in practice, he has been able through the past year of research to break a number of security controls pertaining to Azure AD device security, and demonstrated in his presentation both how this can be done, as well as what the consequences are of those attacks. The video for this presentation is available here.



RIFT, Threat Intelligence, CIRT, and Honeypots

NCC Group’s Research & Intelligence Fusion Team (RIFT) leverages our strategic analysis, data science, and threat hunting capabilities to create actionable threat intelligence, ranging from indicators of compromise and detection capabilities to strategic reports on tomorrow’s threat landscape. To ensure that our managed services remain effective against the latest threats, NCC Group operates a Global Fusion Center – a multidisciplinary team that converts our cyber threat intelligence into powerful detection strategies. The work described in this section includes the work of RIFT, as well as complementary research and insights from NCC Group’s Global Incident Response/CIRT teams.

  • In January 2021, Ollie Whitehouse wanted to build a mechanism to capture all the passwords used (successful or not) against RDP in order to provide blue teams with a source of high-signal intelligence around potential credential compromises. His blog post, Building an RDP Credential Catcher for Threat Intelligence, provides the background on an approach and the steps to build such a system. This post described how to build an RDP credential catcher for threat intelligence, with the initial caveat that he had to disable NLA (Network Level Authentication – an authentication method where a user is initially authenticated via a hash of their credentials over RDP) in order to receive the password in cleartext. However, RDP clients of course may be attempting to connect with NLA enabled, which is a stronger form of authentication that does not send passwords in cleartext. In October 2021, this research was extended by Ray Lai and Ollie Whitehouse, as discussed in their blog post Cracking RDP NLA Supplied Credentials for Threat Intelligence. In that second post, they discuss their work in capturing and cracking the hashed passwords being sent over NLA connections to ascertain those supplied by threat actors, and release the associated source code
  • In January 2021, Wouter Jansen published Abusing cloud services to fly under the radar, to provide the wider defensive infosec community with information, intelligence, and historic data  that can be used to hunt for the threat actor threat in historic data and improve detections for intrusions by this intrusion set. The APT group, known as “Chimera,” has been best known for abusing cloud services from Google and Microsoft to seek everything from intellectual property (IP) from victims in the semiconductor industry, through to data from the airline industry. 
  • On January 23rd 2021, NCC Group’s Research and Intelligence Fusion Team (RIFT) published a blog post titled Analysing a Lazarus Shellcode Execution Method. This post begins with a discussion of techniques used by Lazarus Group used to execute shellcode from VBA (initially identified in a malware sample shared by Check Point Research two days prior), as well as the known modus operandi of the threat actor of phishing using macro documents disguised as job descriptions in LinkedIn (as documented in research by ESET). In their blog post, NCC Group’s RIFT extended this research by analyzing sample macro-enabled documents, and pivoting on the macro, to identify a number of other similar documents for further analysis. In these documents they came across a new technique being used to execute shellcode from VBA without the use of common “suspicious” APIs, whereby they abuse (otherwise-presumed) benign features of the Windows API to achieve code execution. This work was covered by Hacki9
  • In March 2021, Rich Warren and Sander Laarhoven published a blog post titled RIFT: Detection capabilities for recent F5 BIG-IP/BIG-IQ iControl REST API vulnerabilities CVE-2021-22986. In this post, they discuss the wild exploitation attempts and detection logic for the F5 BIG-IP/BIG-IQ iControl REST API vulnerabilities CVE-2021-22986.
  • On April 23rd 2021, our Research and Intelligence Fusion Team (RIFT) published some statistics around deployment of Pulse Connect Secure versions in the wild. The hope was that by releasing these statistics, RIFT could help to highlight the risk to enterprises around outdated versions of Pulse Connect Secure, which are being actively exploited by state-level threat actors. This message further amplifies the concerns shared in this joint advisory from CISA, the NSA, and the FBI from April 15th, which discussed CVE-2019-11510, in addition to subsequently-disclosed vulnerabilities including CVE-2021-22893. 
  • In April 2021, Michael Gough presented “Incident Response Fails – What we see with our clients, and their fails, preparation will save you a ton of $$$, heartache, maybe your sanity and job,” and Aaron Crawford presented, “Exploring the Hacker Mentality for Positive Solutions,” both at BSides Oklahoma. In June 2021, Michael Gough also presented his Incident Response Fails talk at BSides SATX.
  • In May 2021, our Research and Intelligence Fusion Team (including ​​fumik0_) published RM3 – Curiosities of the wildest banking malware – a blog post resulting from over 2 years of their work tracking the Gozi malware variant, RM3. In this post, they provided detailed history, analysis and observations on the most pernicious family of banking malware targeting Oceania, the UK, Germany and Italy, giving an overview of Gozi’s origins and current operations, and then offering a deep dive technical analysis of the RM3 variant, which RIFT has observed to have targeted at least 130 financial institutions.
  • In May 2021, Aaron Greetham published Detecting Rclone – An Effective Tool for Exfiltration. This post discusses NCC Group’s Cyber Incident Response Team’s (CIRT) experience responding to a large number of ransomware cases where the open source tool Rclone is being used for data exfiltration by threat actors. In this post, he builds on the defensive and threat intelligence work by others and provides additional methods of detection, including Sigma rules to assist with hunting in your own environment. 
  • In June 2021, Michael Matthews and William Backhouse published a blog post, Handy guide to a new Fivehands ransomware variant, describing their observation of a new variant of the FiveHands ransomware, deployed by a threat actor matching several characteristics shown through the campaign which suggested a link to UNC2447. This blog post aims to help defenders through offering a description of the developments in the ransomware variant as well as the techniques and attack toolkit used by the ransomware operator. This research was covered by ITPro Today and ThisWeekin4n6.
  • In July 2021, Michael Gough published a blog post titled Detecting and Hunting for the Malicious NetFilter Driver. This work discussed recent allegations by security researchers from G Data that a driver for Microsoft Windows named “netfilter.sys” had a backdoor added by a 3rd party that was subsequently signed by Microsoft as a part of the Microsoft OEM program. This post offered details (including IoCs) about the malicious driver, the attack, and the post-exploitation process to help defenders with threat hunting and detection. It was covered by ThisWeekIn4n6
  • In August 2021, Sanne Maasakkers presented Phish like an APT: Phenomenal pretexting for persuasive phishing at the DEF CON Adversary Village and later in the year, at Ekoparty. In this talk, Sanne presented an analysis of hundreds of phishing emails that were used in real campaigns. All characteristics of an email, like the method of influence, tone of speech and used technologies are classified. By comparing and measuring the state of these phishing emails, she shared how we can learn more about how certain groups operate, and critically, how real-world APT phishing strategies differ from red team exercises. You can watch a recording of her DEF CON version of the presentation here.
  • In August 2021, Michael Gough wrote a blog post on the importance of disabling office macros, where he offers methods for detection and prevention of malicious macros.
  • In September 2021, Michael Gough published a blog post titled Detecting and Hunting for the PetitPotam NTLM Relay Attack. This post discussed the proof of concept tool named “PetitPotam” released by security researchers a few weeks earlier, which exploits a flaw in Microsoft Windows Active Directory Certificate Servers with an NTLM relay attack. The flaw allows an attacker to gain administrative privileges of an Active Directory Certificate Server once on the network with another exploit or malware infecting a system. This post provided details to assist organizations in detecting and threat hunting for this and other similar types of threats.
  • In October 2021, the Research and Intelligence Fusion Team published SnapMC skips ransomware, steals data, which discussed NCC Group’s observation in late 2021 of an increase in so-called data breach extortion cases, whereby an attacker steals data and threatens to publish said data online if the victim decides not to pay, all without use of ransomware. Specifically, RIFT has observed an adversary with a consistent pattern of behaviours whom they track as “SnapMC” – so-named because of both the speed of the actor’s attacks, which are generally completed in under 30 minutes, as well as the exfiltration tool mc.exe it uses. This adversary has not yet been linked to any known threat actors. This post details both the psychological tactics used on victims of SnapMC, as well as techniques used and potential mitigations. 
  • In October 2021, Michael Gough published Detecting and Protecting when Remote Desktop Protocol (RDP) is open to the Internet, in which he explains the dangers of exposing RDP to the internet (including that 42 percent of ransomware cases in Q2 2021 leveraged RDP compromise as an attack vector), and provides details to assist organizations in detecting, threat hunting, and reducing malicious RDP attempts.
  • In November 2021, RIFT published TA505 exploits SolarWinds Serv-U vulnerability (CVE-2021-35211) for initial access, in which they discuss how an observed increase in Clop ransomware victims enabled them to trace the surge back to a vulnerability in SolarWinds Serv-U that is being abused by the TA505 threat actor, known for extortion attacks using the Clop ransomware. RIFT believes that exploiting such vulnerabilities is a recent initial access technique for TA505, deviating from the actor’s usual phishing-based approach, and published this information both as a call to action for organizations using SolarWinds Serv-U software, as well as to inform incident responders currently dealing with Clop ransomware. 
  • In November 2021, Pepijn Hack & Zong-Yu Wu presented “We Wait, Because We Know You” – Inside the Ransomware Negotiation Economics at Black Hat Europe 2021. This session explored three main topics. First, can we explain how adversaries use economic models to maximize their profits? Second, what does this tell us about the position of the victim during the negotiation phase? And third, what strategies can ransomware victims leverage to even the playing field? To answer these questions, the researchers analyzed over seven hundred attacker-victim negotiations between 2019 and 2020. Also at BHEU, Pepjin Hack (NCC Group), Kelly Jackson Higgins (Dark Reading), & Rik Turner (Omdia) hosted a panel titled Ransomware as the New Normal, in which they discuss why ransomware actors are hard to stop, and what organizations can do to improve their defenses against these debilitating attacks. Later that month, they published, “We wait, because we know you.” Inside the ransomware negotiation economics, which complemented their BHEU presentation, covering both the economics of ransomware attacks, as well as negotiation strategies to use with threat actors when a ransom is being demanded.
  • In December 2021, Michael Gough presented a talk titled ARTHIR: ATT&CK Remote Threat Hunting Incident Response Windows Tool at the Open Source Digital Forensics Conference. In this talk Michael discussed ArTHIR, a modular framework that can be used remotely against one, or many target systems to perform threat hunting, incident response, compromise assessments, configuration, containment, and any other activities one can conjure up utilizing built-in PowerShell (any version) and Windows Remote Management (WinRM), and enables you to map your threat hunting and incident response modules to the MITRE ATT&CK Framework.
  • In December 2021, RIFT continued their exposition on threat actor TA505 in the blog post, Tracking a P2P network related to TA505, written by Nikolaos Pantazopoulos and Michael Sandee. As outlined in some of the items above, for much of 2021 RIFT had been closely tracking the operations of TA505 and the development of their various projects (e.g. Clop). During the research in question, the researchers encountered a number of binary files that we have attributed to the developer(s) of ‘Grace’ (i.e. FlawedGrace). These included a remote administration tool (RAT) used exclusively by the thread actor, TA505. The identified binary files are capable of communicating with each other through a peer-to-peer (P2P) network via UDP. While there does not appear to be a direct interaction between the identified samples and a host infected by ‘Grace’, they believe with medium to high confidence that there is a connection to the developer(s) of ‘Grace’ and the identified binaries. In this post, they offered a history of TA505 beginning as early as 2014, and offered technical analysis of the downloader, signed driver, and node tool aspects of the execution chain.


Managed Detection & Response

In a new blog post, "Detecting Anomalous TLS Certificates with the Half-Space-Trees Algorithm," we discuss how we used machine learning to identify anomalous TLS certs used by #ransomware operators, obtaining powerful info without compromising encryptionhttps://t.co/WckKtReJFF pic.twitter.com/pGgUcLsyOp

— NCC Group Research & Technology (@NCCGroupInfosec) December 2, 2021


Exploit Development Group

NCC Group’s Exploit Development Group (EDG) is a small team of full-time exploit developers who write custom exploits exclusively for the purpose of helping our clients test their own infrastructure and systems against real-world attacks of contemporary vulnerabilities and exploits in the wild, to better understand their risk and resilience. This team reports directly into Group CTO, Ollie Whitehouse. Sometimes, this team presents some of their research externally, and occasionally will speak publicly about consensual, proof-of-concept exploitation of vulnerabilities on our clients’ infrastructure, such as in our previous discussion of how in 2017, we unleashed our version of NotPetya on global commodities trading firm, Trafigura.

  • In March 2021, Cedric Halbronn published Wubes: Leveraging the Windows 10 Sandbox for Arbitrary Processes. Through this post, he released his new Wubes, which offers Qubes-like containerization but for Microsoft Windows. The idea is to leverage the Windows Sandbox technology to spawn applications in isolation. The initial release of Wubes supported spawning a Windows Sandbox for the Firefox browser but other applications could be easily added The rationale for creating Wubes was that if you browse a malicious site using Wubes, it won’t be able to infect your Windows host without additional chained exploits. Specifically, this means attackers need 1, 2, 3 and 4 below instead of just 1 and 2 in the case of Firefox:
    • 1) Browser remote code execution (RCE) exploit
    • 2) Local privilege exploit (LPE)
    • 3) Bypass of Code Integrity (CI)
    • 4) HyperV (HV) elevation of privilege (EoP)
  • In June 2021, Cedric Halbronn published Exploit mitigations: Keeping up with evolving and complex software/hardware, a knowledge base of exploit mitigations available across numerous operating systems (Windows, Linux, Android, iOS, OpenBSD, FreeBSD), architectures (ARM) and applications and versions, including the glibc library, Mozilla Firefox, Microsoft Edge, Google Chrome, and Microsoft Office. 
  • In June 2021, Cedric Halbronn published Exploiting the Sudo Baron Samedit vulnerability (CVE-2021-3156) on VMWare vCenter Server 7.0. This post detailed the technique of abusing defaults structures to exploit CVE-2021-3156 (which was originally made public by researcher Worawit, but not fully explained), and allows privilege elevation from the regular user vsphere-ui to root. As a part of this post, Cedric also released a new version of the libptmalloc tool, which is heap analysis tooling for ptmalloc (pthreads malloc), and is interesting to those seeking to exploit glibc. In this post he also made public an updated version of the exploit that is more robust and works on vCenter Server.
  • Beginning in July 2021, Alex Plaskett published a series of blog posts on CVE-2021-31956, Exploiting the Windows Kernel (NTFS with WNF). These posts looked at CVE-2021-31956 (NTFS Paged Pool Memory corruption), a local privilege escalation within Windows due to a kernel memory corruption bug which was patched within the June 2021 Patch Tuesday. He attempted to exploit this vulnerability on Windows 10 20H2 to determine the ease of exploitation, and to understand the exploit mitigation challenges attackers face when writing a modern kernel pool exploits for Windows 10 20H2 and onwards. The first blog post in the series described the vulnerability, the initial constraints from an exploit development perspective and finally how Windows Notification Framework can be abused to obtain a number of exploit primitives. The second blog post in the series, CVE-2021-31956 Exploiting the Windows Kernel (NTFS with WNF) – Part 2, described improvements which can be made to an exploit to enhance reliability, stability and clean-up afterwards, including exploitation without the CVE-2021-31955 information disclosure, enabling better exploit primitives through PreviousMode, and some thoughts on detection.
  • In November 2021, NCC Group’s Exploit Development Group (Aaron Adams, Cedric Halbronn, and Alex Plaskett) participated in Pwn2Own Austin 2021, where they successfully exploited the Lexmark MC3224i printer with a file write bug, as well as gaining code execution on the Western Digital PR4100 NAS. This work was supported by Catalin Visinescu to retrieve firmware from one of the devices and Matt Lewis and Matt Trueman with equipment procurement and logistics support.


Other Research & Speaking

Not sure if Microsoft fixed this (my VM is unpatched). But it works in explorer preview mode via RTF: https://t.co/GI9xr71JKt pic.twitter.com/H5cdmL8tpX

— Rich Warren (@buffaloverflow) September 8, 2021


Acknowledgements 

Research is an intrinsic part of many of our technical security consultants’ daily lives, and almost all of the research that you see in this report was delivered by dozens of consultants from NCC Group offices around the world, seconded into Research to work on their passion projects, empowered by a few thousand dedicated research days across the Group, led by just 1-2 full time staff overseeing end-to-end the over 237 independent research projects delivered in 2021. I’m really proud of this, both because it is a radically inclusive model where every interested consultant can take part and grow as a researcher, and also because we are a lean team that accomplishes a tremendous amount of meaningful things and wastes nothing.

My first acknowledgement goes out to all of our consultants who spent part of their time in the Research division this year, without whose talent, curiosity, time, and courage our research simply would not exist.

I would also like to thank Aaron Haymore, our Research Program Coordinator, for his contributions this year to our research program, including help with countless coordinated vulnerability disclosures, administrative and program management support, his assistance in compiling some of this information for this report, and the joy he brings to everything we do.

I am also grateful to our Research Working Group Leads and RDs – each a talented researcher and consultant in their own right – who generously share their time to mentor others, lead technical discussions in our monthly research working group meetings, and even inspire new projects. These leaders include Daniel Romero, Jeff Dileo, Jeremy Boone, Nick Dunn, Richard Appleby, Robert Seacord, Timur Duehr, Viktor Gazdag, and William Groesbeck.

Finally, I would like to thank our US CTO, Dave Goldsmith, our Group CTO, Ollie Whitehouse, and our Commercial Research Director, Matt Lewis, for their friendship and for all of the many ways in which they help to support and advance Research at NCC Group, as well as to thank the artists at MC2 Manchester for the graphic design of this report.

Tool Release – insject: A Linux Namespace Injector

8 January 2022 at 05:20

tl;dr Grab the release binary from our repo and have fun. Also, happy new year; 2021 couldn’t end soon enough.

Background

A while back, I was asked by one of my coworkers on the PSC team about ways in which to make their custom credit card data scanner cloud native to assess Kubernetes clusters. While the large quantity of advice and sympathy I offered them is out-of-scope for this post, the thought of such a tool got me thinking about ways to get external code running inside of running containers, especially read-only ones.

Note the phrase “external code,” as in code not already in a container image. This is the main problem that makes nsenter completely useless and an absolute headache to use; you can either run a binary from the container, or you can run your binary in every namespace of the container but the mount namespace. This is “fine” when all you want to do is some limited network namespace testing, but it’s a nonstarter when you want to grep across the filesystem that the container actually uses or interact with its actual procfs.

Obviously, outside of injecting code into a process already in a namespace, any implementation is going to involve setns(2). The problem is that you need to somehow call setns(2) after the the execve(2) syscall, and ideally after the early init of the program being thrown into a foreign namespace.

Introducing insject, Your Friendly Neigh^H^H^H^Hamespace Injection Utility

insject started out life as proof-of-concept C program and then an lldb script version. It is now a Rust program compiled into a dual shared library-executable; more on that later. insject’s primary feature is that it enables you to do all of your container attach fun after execve(2)-ing. Its secondary feature is the flexibility it offers in doing so.

insject is fundamentally an LD_PRELOAD library that uses libfrida-gum to set up a hook on a configurable symbol/function/address/etc. (default main()) that, when hit, kicks off the container attach logic (including all of the setns(2) calls). This enables deferring the namespace attaches to basically whenever it is fine to do so. It also does a lot of work to hide its LD_PRELOAD environment variable from the process loaded with it to prevent it from being propagated to child processes; this is a major issue because shells such as Bash do a lot of wacky things with their environment variables.

As is par for the course, insject supports using arbitrary combinations of Linux namespaces based on process PIDs, uid:gid:groups, and AppArmor profiles (defaulting to the profile used by the target process). Additionally, insject supports controlling whether the userns setns(2) call happens before or after the other setns(2) calls, which can be occasionally useful. insject also supports (in LD_PRELOAD-based modes) fork(2)-ing after namespace joins when joining a PID namespace.

insject supports multiple methods of operation:

  • shared library: LD_PRELOAD with CLI-style arguments in an environment variable
  • shared library: LD_PRELOAD with JSON-encoded configuration in an environment variable
  • executable: exec into target command with self as LD_PRELOADed shared library and JSON-encoded configuration environment variable
  • executable: remotely debug a target process into joining namespaces

That’s .so executable

So as to gain maximum compatibility without having to resort to tricks like memfd_create(2), insject is built as a dual shared library-executable so that it may use its own path as an LD_PRELOAD target. Up until recently, such polyglot PIE executables were fairly standard affairs, but glibc in their infinite fatuity decided that such things should not be allowed to exist and on recent glibc versions, dlopen(3) will reject PIE executables. To get around this, we use the standard mechanism of removing the PIE flag from the executable because dynamically linked ELF executables are shared objects. However, due to this chicanery, there appear to be some weird edge cases with ld.so and/or Rust that result in the LD_PRELOAD execution environment being a bit finicky with such binaries, resulting in segfaults in seemingly non-unsafe code; so it’s very likely a glibc hubris bug.

Unfortunately, due to weird issues I’m still working out, debuggers seem to have issues with injecting these kinds of binaries into processes, so the remote debug mode is currently a C version of the insject payload packed into an lldb call expression.

The other oddity with the build is that Rust/cargo don’t really like linking arbitrary statically linked libraries (.a files) into shared libraries, which insject can be built as. And they also have all sorts of annoying issues with library lookup paths for such things, especially when compiling C as part of the build process, which insject intrinsically does due its dependence on my binding wrapper for frida-gum’s gum_interceptor_attach API, which relies on GLib-ified C. So I updated that crate to provide a build.rs macro to place a .cargo/config file with the necessary rustflags. Meanwhile, Rust is handing out CVEs for whitespace tricks, *tsk* *tsk*.

Usage

The normal method of using insject is to just execute it, pass in the target PID, a -- separator, and then whatever command you want to run against the target PID’s namespaces.

$ ifconfig 
br-1a3c6b64c540: flags=4099<UP,BROADCAST,MULTICAST>  mtu 1500
        ether 02:42:b2:46:70:9a  txqueuelen 0  (Ethernet)
        RX packets 0  bytes 0 (0.0 B)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 0  bytes 0 (0.0 B)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

docker0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
        ether 02:42:bb:ee:b4:64  txqueuelen 0  (Ethernet)
        RX packets 0  bytes 0 (0.0 B)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 26  bytes 5660 (5.6 KB)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
...
$ sudo insject -S <pid> -- sh
[insject] -> mnt: 0, net: 0, time: 0, ipc: 0, uts: 0, pid: 0, cgroup: 0, userns: -1, apparmor: docker-default, user: 0/0/0
# ifconfig
eth0      Link encap:Ethernet  HWaddr 02:42:AC:11:00:02  
          inet addr:172.17.0.2  Bcast:172.17.255.255  Mask:255.255.0.0
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:40 errors:0 dropped:0 overruns:0 frame:0
          TX packets:0 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0 
          RX bytes:23261 (22.7 KiB)  TX bytes:0 (0.0 B)

lo        Link encap:Local Loopback  
          inet addr:127.0.0.1  Mask:255.0.0.0
          UP LOOPBACK RUNNING  MTU:65536  Metric:1
          RX packets:0 errors:0 dropped:0 overruns:0 frame:0
          TX packets:0 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:1000 
          RX bytes:0 (0.0 B)  TX bytes:0 (0.0 B)
$ sudo ./target/release/insject -S -s setns <pid> -- python3
Python 3.8.10 (default, Nov 26 2021, 20:14:08) 
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> open("/etc/hostname","r").read()
'ubuntu\n'
>>> import sys, os, ctypes, subprocess; libc = ctypes.CDLL('libc.so.6'); libc.setns(-1, -1)
[insject] -> mnt: 0, net: 0, time: 0, ipc: 0, uts: 0, pid: 0, cgroup: 0, userns: -1, apparmor: docker-default, user: 0/0/0
-1
>>> open("/etc/hostname","r").read()
'af40a9036a27\n'

Wait, setns(2) After execve(2) as root, Isn’t That Dangerous?

Tremendously so. There is a reason why insject sets the PID namespace towards the end. If it did so earlier, the target namespace could potentially ptrace(2) you before you finished leaving your namespace, and while you were still holding significant privileges. In fact, this is actually a fun way to enable cross-parent ptrace(2) support in contravention of yama.ptrace_scope; using user namespaces; you have the target join your user namespace that you have CAP_SYS_PTRACE in, and then you ptrace(2) it.

However, in practice, the real risk here is the file descriptors and the willingness of the binary you want to inject to load arbitrary code from the new mount namespace’s filesystem. This can be a big issue with Bash, which really likes to load shell scripts or with interpreters such as Python, which will prioritize local files in the directory (it thinks is) of the script being run over standard library modules, due to how sys.path works.

A Short List of Things Not To Do

  • With a program that loads code from disk at runtime, attach to any normal container’s mount namespace without attaching to the PID namespace and forking (the default for PID ns attach)
    • If you don’t do both, you run the risk of loading code into a process that still has access to the host PID namespace and likely still has privileges there.
  • With a program that loads code from disk at runtime and holds writable (or directory) file descriptors to host resources from before being insjected, attach to any normal container’s mount namespace
    • Doing so runs the risk of loading code that then uses the existing file descriptors to host namespace resources to manipulate them (so don’t do something like open("/", 0) before insjecting)
  • Attach to the PID namespace of a container with CAP_SYS_PTRACE, but not other namespaces, especially the mount namespace
    • If you do this, even without pre-forking, subprocesses can be ptrace(2)-d by container processes via their PID and used to access host namespace resources
  • With a program that holds writable (or directory) file descriptors to host resources from before being insjected, attach to a container with CAP_SYS_PTRACE
    • Similarly to the previous bullet, subprocesses or the fork child can be ptrace(2)-d by container processes via their PID and used to access the host namespace resources via their file descriptors.
  • insject a program after it has created additional threads
    • As setns(2) only assigns a namespace to the calling thread, insjecting after a process has created threads will mean that the process will exist in a state where one of its threads will enter separate namespaces, while the others will remain in their original namespaces. This results in a similar risk profile as the previous situations as code execution in one thread can be trivially pivoted into another thread that can operate in the original namespaces. Additionally, setns(2) simply does not work with multithreaded programs for user namespaces and time namespaces, though the latter behavior is undocumented. setns(2) will also not work if any threads have been created with the clone(2) CLONE_FS flag, which is used to ensure that chdir(2) and umask(2) syscalls are applied across threads; this flag is used for all Golang runtime threads, resulting in post-runtime init Go processes not being able to join mount namespaces, time namespaces, and user namespaces.

There are likely a large number of other dangerous combinations that arise from attaching to semi-privileged containers in specific ways, but they likely generally involve privileges that would enable container breakout anyway. However, your best bet is to use the -S,--strict flag, which will exit(2) the process if the insject operation occurs while the process has multiple threads, or if any part of the operation fails.

So be very careful. Like use /bin/sh (dash, ash, etc.), not bash or zsh, careful, especially if you have custom shell configurations/integrations like rvm. You have been warned. insject is available at https://github.com/nccgroup/insject, have fun.

Conclusion

This was a fun little toy to put together. insject is a fairly useful utility and solves most of the problems of setns, so I’m fairly happy with it. I will continue to try to find a good solution to the debugger problems, but it might have to involve a custom ptrace(2)-based code injector, which would potentially help with the other main limitation of LD_PRELOAD and dlopen(3), they don’t really work from statically linked binaries.

Separately, while this isn’t my first time using Rust to build intricate LD_PRELOAD payloads, for all the annoyances involved, I would like to make it clear that Rust is still a great language to work with for these kinds of tasks since you have full access to its standard library and crate ecosystem.

Detecting anomalous Vectored Exception Handlers on Windows

3 January 2022 at 16:24

tl;dr

  • At least one commercial post exploitation framework is using Vectored Exception Handling (VEH) on Microsoft Windows to hook functions in an attempt subvert hooking detection.
    • e.g. We recently released a Copy on Write Detector which is capable of detecting patches to EtwEventWrite via traditional memory patching methods without the need for byte comparison.
  • NCC Group researched a way to enumerate and identify anomalous and/or potentially malicious VEH handlers present on a host.
  • We have documented our approach and shared example code which is capable of identifying:
    • if a process is using VEH or not
    • which handlers are present and which modules they point to
    • highlight if they don’t point to a known module

Prior Work

This research was aided significantly by the prior work of others:

Problem Statement

In short we want to be able to:

  • Enumerate which processes are using VEH
  • Identify how many VEH handlers are present
  • Identify which loaded modules those VEH handlers are serviced by

Malicious VEH Usage

VEH use is a well known technique in the gaming community as a means of bypassing integrity checking code used by games publishers. As such some in the cyber research community have adopted it as a technique for similar reasons.

An example malicious VEH installation would look something akin to:

void InstallHandler()
{
LPVOID myMalHandler = NULL;
// Allocate some memory
myMalHandler = VirtualAlloc(NULL, 1000, MEM_COMMIT | MEM_RESERVE, PAGE_EXECUTE_READWRITE);
//
// Copy shellcode or similar into myMalHandler here
//
// Add the handler
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)myMalHandler);
}
view raw MalHandler.cpp hosted with ❤ by GitHub

Our objective is to detect such use.

Solution

VEH using Process Enumeration

Using the Process Environment Block (PEB) there is the CrossProcessFlags bit mask which provides an indicator if a process is or is not using VEH.

To enumerate if a process is using VEH we:

  • Open a handle to the process via OpenProcess
  • use NtQueryInformationProcess to get process basic information which include PebBaseAddress
  • use ReadProcessMemory to read the PEB and check the CrossProcessFlags bit mask for 0x4 (VEH)

This provides a performant way to do initial process triage.

VEH Handler and Handling Module Enumeration

As documented by others previously the VEH handlers are stored in a doubly linked list. The address for the start of the doubly linked list is in an unexported variable called LdrpVectorHandlerList in ntdll.dll. The address LdrpVectorHandlerList is obtainable from symbols or using a heuristic on RtlpCallVectoredHandlers without.

Once we have the address of LdrpVectorHandlerList we walk the list and its associated structure. It was at this point sadly that Dimitri’s excellent prior work did not translate to Windows 10 x64.

The structure we ended up using was:

#pragma pack(2)
struct VEH_HANDLER_ENTRY
{
	LIST_ENTRY Entry;
	PVOID UnKwn1;
	PVOID UnKwn2;
	PVOID VectoredHandler3;
};

With this structure we are able to:

  • use ReadProcessMemory using the address of LdrpVectorHandlerList
  • walk the doubly linked list extracting VectoredHandler3 from each VEH_HANDLER_ENTRY
  • decode VectoredHandler3 – it is encoded via EncodePointer – to get the handler’s virtual address
  • For each of the decoded pointers then identify via EnumProcessModules and GetModuleInformation which modules (i.e. executables or libraries) each of the handlers point to.

With this done we are able to enumerate both which processes are or are not using VEH.

But also how many handlers are present for a particular process and which modules are serving them.

Identifying Anomalous or Malicious Handlers

VEH use doesn’t seem overly common in the unscientific sample size of one host. But what does an anomalous or malicious handler look like in the output? To test the concept we used the following as a cartoon:

void InstallHandlers()
{
AddVectoredExceptionHandler(true, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter);
_tprintf(TEXT("[i] Registered exception handler %p\n"), &MyUnhandledExceptionFilter);
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter2);
_tprintf(TEXT("[i] Registered second exception handler %p\n"), &MyUnhandledExceptionFilter2);
// Register the third
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter3);
_tprintf(TEXT("[i] Registered third exception handler %p\n"), &MyUnhandledExceptionFilter3);
// Register the fourth
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter4);
_tprintf(TEXT("[i] Registered fourth exception handler %p\n"), &MyUnhandledExceptionFilter4);
// Register the fourth lots
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter4);
_tprintf(TEXT("[i] Registered fourth exception handler %p\n"), &MyUnhandledExceptionFilter4);
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter4);
_tprintf(TEXT("[i] Registered fourth exception handler %p\n"), &MyUnhandledExceptionFilter4);
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter4);
_tprintf(TEXT("[i] Registered fourth exception handler %p\n"), &MyUnhandledExceptionFilter4);
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)MyUnhandledExceptionFilter4);
_tprintf(TEXT("[i] Registered fourth exception handler %p\n"), &MyUnhandledExceptionFilter4);
LPVOID myMalHandler = NULL;
// Allocate some memory
myMalHandler = VirtualAlloc(NULL, 1000, MEM_COMMIT | MEM_RESERVE, PAGE_EXECUTE);
AddVectoredExceptionHandler(false, (PVECTORED_EXCEPTION_HANDLER)myMalHandler);
}

Using our approach we can clearly see the last ‘mal’ handler points to a region of memory which isn’t hosted by a DLL or EXE.

Further work can be done here to ensure that the exception handler pointers reside in the .text section and that the section is integral.

Summary and Conclusions

In this post we have documented the appeal of VEH use on Windows to avoid certain hooking detection techniques. We have also shown VEH use is easy to detect due to a low volume of usage coupled with high signal indicators when anomalously used – all in a performant manner.

We encourage EDR, telemetry and other observability vendors to integration these enumeration techniques into their Windows product lines to facilitate detection by cyber defense teams.

Code

The example code can be obtained from here.

However please note it is only example code , it is only known to work on Windows 10 x64

Footnotes

Some footnotes associated with the project.

x64dbg

The x64dbg team have support for dumping the VEH – however it produces incorrect pointers on x64 Windows 10 – we reported it and shared our code.

RtlpLogExceptionHandler

It was noted RtlpCallVectoredHandlers contains a call to RtlpLogExceptionHandler

It was hypothesized that this might provide a further source of telemetry / forensic artifacts about which handlers have recently been called even if the handlers were no longer present. However it was unclear how it would be retrieved. In speaking to Alex Ionescu it transpires it first needs to be enabled via gflags

After only which it can be obtained via !exrlog in WinDbg

On the malicious use of large language models like GPT-3

31 December 2021 at 22:30

(Or, “Can large language models generate exploits?”)

While attacking machine learning systems is a hot topic for which attacks have begun to be demonstrated, I believe that there are a number of entirely novel, yet-unexplored attack-types and security risks that are specific to large language models (LMs), that may be intrinsically dependent upon things like large LMs’ unprecedented scale and the massive corpus of source code and vulnerability databases within their underlying training data. This blogpost explores the theoretical question of whether (and how) large language models like GPT-3 or their successors may be useful for exploit generation, and proposes an offensive security research agenda for large language models, based on a converging mix of existing experimental findings about privacy, learned examples, security, multimodal abstraction, and generativity (of novel output, including code) by large language models including GPT-3.

This post is organized as follows:

  1. Introduction to large language models 
  2. GPT-3 as a Case Study:
  3. Known capability: GPT-3 can generate code 
  4. Potential capability: Can GPT-3/Codex generate exploits? 
  5. An Offensive Security Research Agenda for Large Language Models
  6. Thinking about Artificial General Intelligence
  7. Conclusions 
  8. Note from the Author
  9. References & Further Reading

I would caution the reader that unlike the vast majority of what we publish on this blog, this post is purely theoretical and lacks working proofs-of-concept. Like many of the most interesting offensive security research topics related to machine learning and artificial intelligence right now, this one too involves large-scale systems in production that we do not own, which introduces legal, logistical, and ethical barriers against simply hacking on these systems, for which collaborative future research partnerships could be beneficial. Until then, here is an early sketch of some things I’ve been wondering about, why these attacks might be feasible, and speculation about what they might mean for the security of the internet.

1 — Large language models

Natural Language Processing (NLP) is an established subfield of computer science and linguistics which seeks to enable computers to both process and generate information within the natural human languages that people use to communicate. While research in NLP dates back to the mid-20th century, recent work in this field has increasingly over the past decade or so focussed on the use of large neural networks to represent natural languages (most commonly, English). For the purposes of this post, a language model is a type of machine learning model trained upon natural language data, which models the statistical properties of sequences of words in a given language, allowing for the probabilistic prediction of the “next” word in a sequence. (Readers interested in better working definitions of language modeling are directed to Yoshua Bengio’s overview of neural net language models [1]).

Over the last 3-5 years, substantial advancements in (increasingly large) language models have been demonstrated across several industrial research groups from OpenAI, Google, Microsoft, NVIDIA, Facebook, and others, which we’ll explore briefly in the section below. Perhaps the most recognizable of these models to the mainstream is OpenAI’s GPT-2 [2] and more recently GPT-3 [3].

The canonical paper describing GPT-3 is of course “Language Models are Few-Shot Learners” [3] version 1 of which was published in May 2020 by members of OpenAI. This paper describes the team’s approach to training and evaluating the GPT-3 language model, discusses the performance of GPT-3 across a number of NLP (Natural Language Processing) computational benchmarks in the zero/one/few-shot settings, and outlines three categories of “Broader Impacts” which include “Misuse of language models,” “Fairness, bias, and representation,” and “Energy usage.”

While these are clear indicators that the ethics of large language models are a serious consideration for the authors, in the 75-page original GPT-3 paper, the word “security” is mentioned only once – and even in that case, it is only used by analogy to apply threat modelling principles to study non-security threats.

In my view, this is emblematic of what we see across much of the ethical-AI subfield: security is too often merely an analogy or a footnote. The field of ethical AI has often overlooked or under-scoped security, or failed to focus on it at a sufficient level of granularity for it to be properly addressed in real-world systems. Security is critical to ethical artificial intelligence not because it is a parallel property that we should seek alongside things like fairness, but because it is a prerequisite to it. Specifically, security is a prerequisite to ethical artificial intelligence because you cannot make meaningful guarantees about a system that you do not control.

2 — GPT-3 as a Case Study

2.1 — Model & Architecture

As a large language model, GPT-3 has around 175 billion parameters at an estimated training cost of 355 years and at least 4.6 million dollars in 2020 [4]. This is significantly larger than GPT-2, which had around 1.5 billion parameters. This increased size has yielded increased capability in natural language processing. GPT-3 is able to generate texts of up to 50,000 characters, unsupervised, in a variety of styles [5]. GPT-3 is actually “only” three orders of magnitude smaller than the human brain, which has around 100 trillion synapses [4]. (However, there are many ways to functionally measure the “size” of the brain, and number of synapses is not necessarily the “best” way of representing the computational power of the brain – but a discussion of this is out of scope for this blog post.)

While GPT-3 is perhaps the best-known large language model, there are several models of comparative size currently in existence. This chart, borrowed from Bender, et al (2021) [6], offers a guide to other recent large LMs and their relative dataset sizes and numbers of parameters (below):

Large language models 2019-2021, from Bender et al.(2021)

Other recent large language models include MegatronLM (NVIDIA) [7], Turing-NLG (Microsoft) [8], GShard (Google) [9], and Switch-C (Google Brain) [10], and RoBERTa (Facebook) [11], among others. Here we see in recent years an an order-of-magnitude scaling of the size of the largest language models – a noteworthy observation given the relationship between size and performance of these models.

Notably, GPT-3 – perhaps the most widely-discussed example of contemporary large language models – is in this blog post used as a lens through which to ask generic questions about the as-of-yet unexplored capabilities of large language models in general, including those listed above. Thus, the concepts in this post represent universal questions regarding large language models, including not only GPT-3, but also any of its’ derivatives or successors (including when trained on or optimized for security-relevant or code-generation-relevant codebases), as well as large language models of all other flavours. Here, the specifics of GPT-3 (and its’ training data and capabilities) are discussed simply so as to provide concreteness of examples.

The authors state in the introduction of the original GPT-3 paper that improvements in text synthesis and other natural language processing tasks observed in transformer-models – from those with 100 million parameters in a paper published in 2018, to those with 17 billion parameters in a paper published in 2020 – has improved in a reliable way with increasing scale of these models, and furthermore that “since in-context learning involves absorbing many skills and tasks within the parameters of the model, it is plausible that in-context learning abilities might show similarly strong gains with scale [3]. Indeed, they proceed to state that testing this hypothesis was the express purpose of the development of GPT-3. This is powerful.

GPT-3 was benchmarked across several NLP datasets as well as some novel additional tasks, under each of three conditions  [3]: 

  • zero-shot learning” where no demonstration is given to the model – all it receives is an instruction in natural language 
  • one-shot learning” where only one demonstration is allowed to be given to the model 
  • few-shot learning” where a small number of demonstrations are given to the model (stated in the GPT-3 paper as, “in-context learning where we allow as many demonstrations as will fit in the model’s context window (typically 10 to 100)”)  

Strikingly, they demonstrate that “Model performance improves with the addition of a natural language task description, and with the number of examples in the model’s context, K. Few-shot learning also improves dramatically with model size. Though the results in this case are particularly striking, the general trends with both model size and number of examples in-context hold for most tasks we study.” (p. 5) [3].

One of the things I found most interesting was that they find that in the few-shot setting, GPT-3 sometimes meets or surpasses the performance of models that have been fine-tuned on topic-specific models and data, which is intriguing because it suggests that sufficiently large generic LMs may soon be able to outperform even the best domain-specific models across a range of domains, and thus that it may be reasonable to think of large language models as “greater than the sum of their parts,” so to speak.

2.2 — Training Datasets

Arguably one of the most interesting things about GPT-3 from the perspective of a security researcher is not merely its’ size, but rather its’ size in conjunction with the specific datasets upon which it was trained. In particular, OpenAI’s published GPT-3 model card [12] notes that GPT-3 was trained on and evaluated against English-language wikipedia, two internet-based book corpora, and most notably, an expanded version of the Webtext dataset, and a version of the CommonCrawl dataset (“filtered based on similarity to high-quality reference corpora”). 

The Webtext dataset [3], [12] is a 40 GB internal OpenAI dataset of 45 million links, obtained by scraping Reddit for all links (except Wikipedia, because it exists in other training data sources) with at least 3 karma [13]. The CommonCrawl dataset “contains petabytes of data collected over 12 years of web crawling. The corpus contains raw web page data, metadata extracts and text extracts[14].

Consequently, the corpora for large LMs raise interesting questions: What types of security-relevant information is either explicitly or implicitly coded within those datasets, what has GPT-3 learned from that data, and what could it mean about the potential autonomous capabilities of large language models (LMs) like GPT-3 trained on these types of corpora?

Recent research has shown that for GPT-3’s predecessor, GPT-2, hundreds of verbatim text strings of training data can be extracted from the production GPT-2 model, which includes sensitive data elements including “(public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs” [15]. This, combined with the discussion of how to extract PII from LMs and find that it gets easier to extract PII as the language models get larger(!) [15], implies that the PII-extraction attacks possible on GPT-2 may very well be possible on its successor, GPT-3. This is certainly itself worthy of further study.

However, I am actually far more interested in a risk we didn’t observe with GPT-2 – namely, that GPT-3 can generate novel code samples, much like it has been shown to be able to generate complex, novel images and prose [3], discussed in the section below.

The semantic relationships and rich conjunctions of features that GPT-3 has been able to map through natural language for stimuli represented as images or text may even have the same underlying semantic relationships for code generation, which I’ll discuss below.

3 — Known capability: GPT-3 can generate code

One of the most compelling findings of researchers exploring GPT-3 is that it is capable of generating code, often from natural (human) language descriptors. This, combined with findings that GPT-3 is capable of generating novel outputs based on highly unlikely combinations of semantic inputs [16] (such as a “armchair imitating an avocado” below). 

“An armchair imitating an avocado,” from OpenAI’s “DALL-E: Creating Images from Text” (2021)

OpenAI has shown that this is by no means limited to the domain of image generation. Indeed, examples of novel synthesis or generative capability include:

  • Conceptual summarization of text, whereby GPT-3 has been shown to be able to summarize a complex text into a conceptually similar but simpler text, both for 2nd grade readers, as well as to create a “TL;DR” for adult readers [25]
  • Conceptual expansion of text, whereby GPT-3 has been shown to be able to generate a detailed essay outline from a simple, high-level input topic, as well as generate novel analogies in human language [25]
  • Language translation (for – and between – both human and machine languages), whereby GPT-3 has been shown to be able to translate between some natural (human) languages, as well as from machine language to human language (in this case, generating a Python docstring from underlying code), and from human language to machine language (in this case, translating an English prompt into a simple SQL query) [25]

This demonstrates that GPT-3 is generically capable of generating novel outputs based upon highly improbable combinations of unrelated features, as well as demonstrating an ability to generate conceptually meaningful extensions to input data in a variety of forms, including machine-readable code.

So then I’d ask: Can a large language model such as GPT-3 generate novel code, perhaps even for conjunctions of properties for which it has not specifically seen a prior training example?

It turns out that the answer is yes, and that people quickly demonstrated a range of these capabilities without any particular training or tuning of the language model to specifically enable code generation.

Early GPT-3 powered code generation tasks have included the following examples:

  • One researcher was able to create a web layout generator in which one may simply type in a description of the layout they want in plain English, and GPT-3 will generate the associated JSX (JavaScript XML) [17] 
  • When you describe a machine learning model in plain English including only the dataset and required output, GPT-3 was able to generate a Keras machine learning/deep learning model [18]
  • Researchers have created an English to regex generator [19] as well as a text to javascript [20] tool
  • When asking natural-language questions in English about a SQL database, GPT-3 was able to generate the associated SQL queries [21]
  • Other researchers have shown that GPT-3 is able to translate natural language into shell commands [22], and vice versa [23]

We also know that GPT-3 in general “shows one-shot and few-shot proficiency at tasks designed to test rapid adaption or on the fly reasoning,” however they, “also find some tasks on which few-shot performance struggles, even at the scale of GPT-3 (p.5) [3]. Thus, a core question about GPT-3’s abilities reduces to whether the task types involved in security tasks like exploit generation are the type for which GPT-3 shows one-shot or few-shot proficiency – particularly in cases where we do not believe there to be many training samples. If the answer is yes, then we are likely to see the weaponization of large language models for offensive security purposes. 

A perhaps natural question that may follow from seeing large language models competently generate code for a collection of arbitrary development tasks is then, “Can large language models like GPT-3 generate exploit code for existing vulnerabilities for which exploits do or do not already exist, or even for some exploitable zero-day vulnerabilities?” 

This question needs to be asked both for extant large language models like GPT-3 on their existing datasets to understand the potential for weaponization of these general-purpose tools. However, this research critically must also extend to help study how these tools – through the introduction of additional training data or reinforcements – could perform if optimized, even somewhat, for vulnerability detection or exploit generation, such as through more targeted training on these tasks or associated codebases. 

To be clear, the answer is: we don’t know.

But the answer is also: it is important for us to find out.

3.1 — Discussion: “Evaluating Large Language Models Trained on Code” (July 2021)

While I was partway through writing this blogpost, OpenAI & collaborators released a research paper titled, “Evaluating Large Language Models Trained on Code” (July 2021) [24] and published a related blog post in August 2021 [25] which made this topic more timely than ever. These publications discussed Codex, which is a GPT-3 derived language model trained on open source code on GitHub, which generates Python code. Interestingly, the authors note that it is a version of Codex that powers GitHub Copilot [26]. The authors note that this project was initially motivated by the discovery during the early work on GPT-3 that GPT-3 was able to generate simple Python programs from supplied docstrings [3]. Indeed, they note that, “this capability was exciting because GPT-3 was not explicitly trained for code generation[24]. While Codex is based upon GPT and the researchers initially sought to build the Codex code lexer using the GPT-3 text tokenizer, they found that the distribution of words on GitHub compared to natural language meant that they needed to modify things to better represent software language (code) instead of natural language (English).

To evaluate the performance of Codex, the authors created and released a benchmarking suite of 164 programming problems with associated unit tests which assess “language comprehension, reasoning, algorithms, and simple mathematics”, which has been released on GitHub, called the HumanEval dataset [27]. The training data for Codex included 54 million public software repositories on GitHub of Python files under 1MB, having filtered out only training data with above-threshold line lengths, low percentages of alphanumeric characters, and that which appeared to be auto-generated [24]. According to the details presented in the paper, there appears not to have been any filtering for correctness or security of the training sample code.

Their findings were ultimately as follows:

  • Language models are made better at generating code when fine-tuned to do so. Regular GPT models which have not specifically been trained on code perform poorly, but descendants of GPT models that are explicitly trained and fine-tuned on code perform well.
  • Increasingly granular fine-tuning on code helps models perform even better. While Codex (“fine-tuned on code“) solves 28.8% of the HumanEval problems, Codex-S (which is “further fine-tuned on correctly implemented standalone functions“) solves 37.7% of the HumanEval problems.
  • Unsurprisingly, the number of model parameters matters. Performance (in this case, pass rate on a benchmarking task) scales with model size, as illustrated in the diagram from [24] below.
  • Reasonable increases in the number of solutions sampled can dramatically improve code-generation performance. While Codex-S was able to solve 37.7% of the HumanEval problems when permitted to generate just a single solution, when it was permitted to generate 100 samples/solutions and be considered to have solved a given HumanEval problem when at least one of the 100 generated code samples satisfied the unit tests, it was able to solve 77.5% of the HumanEval problems.

But what does all of this mean for security?

This figure, from OpenAI’s “Evaluating Large Language Models Trained on Code” (2021), demonstrates the dramatic increases in efficacy of language models when tuned to work specifically on code generation.

In section 7.5, Security Implications of the Codex paper (p.12) [24], the study’s authors describe the following 4 security risks of code-generation large language models: 

  1. Unintentionally-insecure code: The unintentional generation of insecure code by the model that may then be readily trusted and used by a developer [24]
  2. Enabling malware development: Being used to “aid cybercrime,” but for which the authors state, that they “believe that at their current level of capability, Codex models do not materially lower the barrier to entry for malware development [24]
  3. Enabling polymorphism for signature-detection evasion: Malware developers leveraging the non-deterministic nature of code-generation large language models like Codex to make it easier to create polymorphic malware which may more readily evade antivirus systems’ signature-based detection of malicious binaries [24] 
  4. Leakage of sensitive data: Sensitive data elements in the training data may be learned or predicted by the model [24]

These are valuable future research directions.

However, I would also add the following additional potential security risks to this list: 

  • Poor security-related design “choices” for cryptography other security-critical components of systems architecture: For example, in the original Codex paper [24] the authors noted that when they “asked Codex to suggest code that would call cryptographic libraries to generate cryptographic contexts, ….Codex models of varying sizes frequently use clearly insecure configurations.” Worse yet, these flaws were not subtle issues with cryptographic implementations – but rather, basic things like insecure key sizes (for example, RSA keys of fewer than 2048 bits) or symmetric ciphers using insecure cipher modes (for example, AES in ECB mode) [24]. It is not unreasonable, given these basic flaws, to assume that issues may remain in terms of things like key re-use, insufficiently random sources of randomness, and subtle implementation or mathematical flaws, especially given that these seem to appear to have been created generatively alongside other code, based on learning from a large corpus of data.
  • Entrenchment of language-related vulnerabilities: While the authors did cite “unintentionally-insecure code” as a security risk, the risk is potentially greater than merely generating instances of bad code. Rather, I wonder to what extent vulnerabilities persist in code over time. Even when bad sources (such as flawed codebases or less-secure past programming language standards) are updated, do the models immediately reflect more secure programming patterns? Likely not. Consequently, I wonder whether the eradication of security vulnerabilities could be slowed down by code generation language models, because the underlying code generation models are likely to lag behind security improvements to programming language standards, propagating insecure old patterns, further into the future. 
  • Easy, efficient generation of insecure code samples: In qualitative experiments run on GPT-3’s ability to generate short stories, it was found that use of descriptive adjectives in prompts to the model meaningfully influenced the output. As a striking example, the input “a prize-winning short story” led GPT-3 to generate short stories that were scored as better by readers than when GPT-3 was simply told to write “a short story” [32]. Consequently, I wonder if – given conceptual representation and the volume of code upon which the model was trained – asking for an “insecure” version of a function, library, or other code sample could help create subtly flawed malicious code. Indeed, to be able to efficiently generate many of these samples, it is conceivable that an attackers could test them against static analysis tools to attempt to find malicious code samples which are more likely to go undetected.
  • Exploit generation: The potential for these code-generation models to generate offensive code to exploit common software flaws. In particular, I am interested in the question of whether code-generation large language models are capable of generating exploits – and under what conditions this can be successfully achieved for which categories of vulnerabilities.

Most notably absent from the extant literature is of one of the most potentially weaponizable aspects of large language models’ code generation capabilities: software vulnerability exploit generation.

4 — Potential capability: Can GPT-3/Codex generate exploits?

Suggesting that an existing, generic large language model may be able to discover or even exploit vulnerabilities in code is pretty bold, and will require meaningful proofs of concept to verify. It may be tempting to dismiss this speculation as more of the overhyped views of artificial intelligence that so many in our industry have internalized, where the magic of statistics is some kind of security panacea. However, there are properties specific to large language models that I believe suggest otherwise.

A few core properties of GPT-3 that suggest that GPT-3 (or its’ descendants, which could include any large language model with similar properties, especially those used for code generation) may potentially be able to generate exploits of software vulnerabilities include:

  1. There have been numerous examples of GPT-3’s ability to generate working code in a variety of programming languages. This demonstrates that GPT-3’s training data included a sufficiently large number of code samples to enable at minimum the demonstrated capabilities of some minimum ability to generate code across a range of programming languages. This point predates – but is strongly amplified by – OpenAI’s publication of Codex this summer.

  2. Part of GPT-3’s training data included English-language wikipedia, which describes known vulnerability classes in depth, almost always with example code demonstrating the vulnerability, and often with detailed sections on exploitation that also include sample code, and often also with directly linked historical references to historical malware and otherwise insecure code which is likely present elsewhere in the corpus of training data. 

    Furthermore, an OpenAI blog post discussing Codex noted that this GPT-3 descendant was trained on, ” billions of lines of source code from publicly available sources, including code in public GitHub repositories,” and noting that it “is most capable in Python, but it is also proficient in over a dozen languages including JavaScript, Go, Perl, PHP, Ruby, Swift and TypeScript, and even Shell.” This, in conjunction with the fact that “it has a memory of 14KB for Python code, compared to GPT-3 which has only 4KB” lead the authors to conclude that “it can take into account over 3x as much contextual information while performing any task.” [28]

  3. That GPT-3 can generate code from natural language specifications indicates that there may be semantic networks at multiple levels of abstraction, potentially enabling GPT-3 to relate or translate English-language computer science and security concepts to example code. Further evidence for GPT-3’s abstraction capability comes from the March 2021 paper, “Multimodal Neurons in Artificial Neural Networks,” [30] which described the observation by OpenAI researchers that artificial neural networks exhibited multimodal neurons – that is, specific neurons that responded to a specific CONCEPT (such as the concept of “Jennifer Aniston” or “Spiderman” or of the colour yellow or the autumn season…) regardless of the modality in which it was presented (photo, video, image of the person’s name, etc). Multimodal neurons were first reported to have been observed experimentally in humans in a canonical 2005 Nature paper [30], and were revolutionary because they showed concrete, physiological evidence of the granular substrates underlying abstract, complex, conceptual thinking. Similarly, to know that multimodal neurons are being observed in machine learning models similar to large language models indicates that these models, too, have some level of abstract, conceptual representation, suggesting that logical reasoning about abstract computer security concepts is within the realm of reasonable.

  4. Experiments on GPT-3 shared by researchers, demonstrate GPT-3 having a willingness to construct highly unlikely (and thus it may be inferred: truly novel) artifacts, such as a “giraffe-shaped button,” indicating some ability to be truly generative and creative on the basis of a conjunction of input features to create a novel output, as opposed to the model simply responding verbatim with existing related results from its’ training data.

Ultimately, this is a topic worthy of future study by security researchers, and it is for this reason that I present an initial offensive security research agenda for large language models further into this post. 

From OpenAI’s “Multimodal Neurons in Artificial Neural Networks” (2021). this image offers simple examples of multimodal neurons, something which has been observed in both human biological neurons as well as in artificial neural networks, and is indicative of the possibility for abstract, conceptual reasoning.

5 — An Offensive Security Research Agenda for Large Language Models

In this section, I propose a number of initial open research questions to help us better understand the security properties and potential for weaponization of large language models, examining large language models’ training data, knowledge extraction/data exfiltration, vulnerability creation & detection, code generation, and generic ability to abstract and reason.

Theme 1: Weaponizability of Training Data

1.1 – Learning from Dangerous Codebases
Does the language model in question trained upon data which includes vulnerabilities, their patches, malicious code, or other sources of information about identifying or creating dangerous code? For example, was the language model trained upon:

  • Major malware repositories (source code and/or binaries)? 
  • Open source offensive security tools or proof-of-concept vulnerability exploits published on source code repositories such as GitHub?
  • Technical or security advisories published by software vendors and security researchers?
  • Databases describing vulnerabilities, including (for example) the National Vulnerability Database, whereby data samples of labelled vulnerabilities exist? 
  • Vulnerability-finding tools’ source code and/or output/findings?
  • Multiple copies of source code samples or repositories over time, such that diffs may be computed and security patches associated to specific CVEs be inferred?

Theme 2: Knowledge Extraction & Data Exfiltration

2.1 – Extracting Training Data
It has been demonstrated in some recent large language models that sensitive training data elements, including PII, can be extracted by end users of the model [15]. This presents the question: How at-risk are large LMs’ training data elements, and what are appropriate mitigations of this risk? For example, one may wish to explore:

  • Does the amount of training data that can be extracted from an LM vary between different large language models? If so, what differences between these various models’ architectures, training datasets, approaches to training, and other factors may explain any differences in data leakage?

Theme 3: Vulnerable Code Generation

3.1 – Unintentional Generation of Vulnerable Code
Does language model-generated source code generated from natural language descriptors contain vulnerabilities? And if so: 

  • Can the origin of those incorrect coding patterns be discerned from the training datasets?
  • Does making queries for a “secure” version of a function or by otherwise adding security-related properties to the query submitted to the LM alter the likelihood of generating insecure code?
  • Does LM-driven code generation contain a greater proportion of vulnerabilities than human generated code?
  • Are there alternative approaches to the development of large language models for code generation that would reduce the likelihood of vulnerable code being generated? Alternately, are interventions more effective at a different point in the SDLC/code development pipeline? For example, how could static analysis tools be integrated into workflows that use AI-driven code generation tools, to reduce the number of vulnerabilities in a resulting codebase?

3.2 – Intentional Generation of Vulnerable Code
If a language model is asked in natural (i.e.: human) language to write a function with a specific vulnerability class present in the code, can it generate an instance of this insecure code? And if so, can it generically do this:

  • In many or all programming languages for which the vulnerability class exists? 
  • As a modification to an arbitrary, existing target codebase?
  • Can extant large language models do this when asked in a natural language, such as English, to generate a specifically “insecure” version of a function?

3.3 – Robustness of Code Generation Patterns Over Time
Does language model-generated source code reflect particular temporal markers that indicate its generalizability for effective code generation will degrade over time?

  • Is there a presence of unsafe coding practices in generated code that have since become deprecated or replaced by recent revisions to programming language standards?
  • Is there an increased likelihood over time for a model to generate unsafe code because the model was trained on datasets for which more examples of a particular vulnerability were present in the wild before it was patched? If so, are there effective ways of training the model to account for more recent knowledge of the vulnerability and its’ security fix?

3.4 – Secure Code from Insecure Data
Can language models trained on inevitably vulnerability-laden datasets reliably generate secure code as output?

  • Is it necessary for models to learn from patches applied to security vulnerabilities in their training codebases, or are there other ways to improve the security of generated code?
  • What model-training frequencies or paradigms will allow language models used for code generation to update their models to reflect security patches applied to training data points?
  • What can be done to mitigate the effects of vulnerable training samples on the security of model-generated code?

Theme 4: Vulnerability Detection

4.1 – Ability to Detect Software Vulnerabilities
The concern about whether language models’ code generation capabilities lead to increased vulnerabilities in codebases are serious and real. It is reasonable to also consider the flip side of the question: If large language models are shown to generate vulnerabilities, are they also an effective tool for detecting software vulnerabilities? Specifically:

  • How do simple vulnerability detection queries using large LMs compare to traditional vulnerability-finding tooling such as fuzzing, SAST/DAST, and other flavours of program analysis?
  • Can a conceptual, multimodal representation of vulnerability classes lead to better detection, or even potentially better exploitability estimates?
  • Are large language models’ demonstrated abstraction capabilities useful in finding vulnerabilities in complex codebases for which simple vulnerability scanners/static analysis tools often fail to suffice?

However, I would note that large language models should not be presumed a security panacea, any more than should any other buzzword in our industry (quantum! blockchain! A.I.!). As is the case with everything that offers some kind of potential computational advantage, what matters is whether the computational primitive (in this case, large language models) is of the right fit for a given problem definition (in this case, vulnerability detection) to offer meaningful improvement in task performance over known alternatives. This is to say: While you could probably use language models or machine learning in general for most problems – including vulnerability detection – you only should when it outperforms existing approaches and is worth the computational cost.

Theme 5: Exploit Generation

5.1 – Benchmarking Offensive Code Generation
What would a benchmark suite of code generation tasks look like? We would seek to test the generic ability of a specific large language model to:

  • Generate arbitrary, syntactically correct source code in a specific programming language or in general? 
  • Generate exploit code for arbitrary instances of known vulnerability classes in a specific programming language?
  • Generate exploit code for arbitrary instances of known vulnerability classes in an arbitrary programming language?
  • Generate exploit code which works against specific existing known vulnerabilities (CVEs) on unpatched systems?

I propose the creation of a benchmarking suite – analogous to those used for benchmarking language models against natural language processing (NLP) tasks – to understand the performance of various language models in terms of code generation, the safety/security of generated code, as well as the offensive capability to generate code which exploits vulnerabilities in the wild. A valuable direction for future research is to specify and ultimately build such a benchmark suite.

5.2 – Language-Specific Code Generation Performance
Does code generation performance of a language model vary across programming languages? If so, are there performance differences (unattributable to relative sample sizes of training data) between:

  • Higher-level languages and lower-level languages? 
  • Languages with English-like syntax compared to those without? 
  • Interpreted, scripted, or precompiled languages? (etc)
  • Programming languages with grammars at different levels of the Chomsky hierarchy of formal grammars? Alternately: programming languages with differences in expressive power?

5.3 – Improving Exploit Code Generation Performance
To understand our attackers (or potential attackers), we must understand the art of the possible. For this reason, we would also seek to know: Does combining known techniques in automatic exploit generation (AEG) with the use of large language models increase the efficacy of exploit generation tools against n-day or even 0-day vulnerabilities?

And if so, what are the potential defenses?

Theme 6: Ethics & Safety Considerations

There are undoubtedly ample other interdisciplinary questions around responsibly conducting this research & development, and these are offered merely as a starting-point.

6.1 – Societal Impact
To better understand the societal impact of security risks introduced by code-generation large language models, we may consider things like:

  • In which domains are code-generation LMs likely to be adopted, and what security controls are typical (or even required) in those verticals?
  • In the event that code-generation LMs create less secure code, what complementary tooling or security testing may best find & remediate those vulnerabilities?
  • In the event that code-generation LMs create more secure code, which fields or industry verticals are most likely to face structural or other barriers to deploying this technology?
  • If security reasons justify the frequent retraining of code-generation LMs to improve security of generated code, what is the environmental impact of that retraining, and what could be done to lessen those computational costs?

6.2 – Safety Considerations
To study the prospect of machine-generated code being unintentionally insecure, we might ask things like:

  • How do the security properties of machine-generated code differ from human-generated code? For example, are there more or less vulnerabilities (in general, of a specific class, or in specific languages or contexts) when the code is generated using a machine learning model given a natural language prompt?
  • Given what is known about model-driven generation of insecure code, should these tools be used to generate code used in safety-critical domains? If so, are specific safeguards or assurances (such as formal verification, etc) required by existing standards or regulations, and would these need to change in the case for machine-generated code compared to human-generated code?
  • Is it practical to create code-generation large language models trained only upon code samples known to be secure? Under test, is it actually true that code generated from a highly-secure training set would very rarely or never itself be insecure?

6.3 – Policy & Law Considerations
Further work by researchers and practitioners in policy and law may include questions such as:

  • Who is legally, ethically, or practically (in terms of vulnerability management) responsible for insecure code generated by a code-generation large language model?
  • Do there exist domains for which the use of large LMs should be restricted, or for which existing law or policy may call the use of large LMs into question?
  • Given the dual-use nature of artificial intelligence in general and large language models in particular, what effects will export controls and other international policies have upon global adoption of these technologies (or lack thereof)?
  • Ethically speaking, are there datasets upon which large LMs should not – without informed, well-justified intent, be trained? Practically speaking, could better curation of code repository training datasets be an effective manner to improve the security of language model-generated code?

6.4 – Open Source Security Considerations
In past presentations [29], [31] I’ve speculated on the impact that research advancements to vulnerability finding at scale could have on the state of open source software security. While beyond the scope of this blog post, I will simply note that scalable vulnerability finding is dual-use in that it can benefit both attackers and/or defenders (depending on who develops or uses it best), and that advancements in the ability to intentionally generate subtly insecure code (to make malicious commits), or in the ability to more reliably generate working exploits (against known codebases) is concerning for the state of all software security, not least that of open source software.

6 — Thinking about Artificial General Intelligence

Looking toward the future, large language models such as GPT-3 (and its peers and successors) seem like meaningful milestones in the pursuit of Artificial General Intelligence (AGI).

While there are still capability gaps between our most powerful language models and most researchers’ definitions of AGI, many of the security risks yet unstudied related to artificial general intelligence can be anticipated by thinking through the offensive security implications of today’s large language models.

Questions we may wish to ask include:

  • What are the security problems for which conventional security tools – including machine learning-augmented security tools – currently perform poorly compared to human experts? What are the properties of those problems that make them inaccessible to current tooling, and what types of computational advancements would reduce the performance barriers between best-in-class tooling compared to human experts?
  • Are there new ways of representing or understanding complex codebases that would improve machine efficacy at finding, remediating, and/or exploiting vulnerabilities within them, or that could augment the abilities of human experts?
  • What types of abstraction between human language and machine languages are most useful to help find, remediate, and/or exploit vulnerabilities in software? How could one supplement the training of large language models to more nimbly or accurately translate between those abstractions? 
  • What does the thought process look like for human exploit developers, beginning at (or before) the moment at which they identify a vulnerability, through to successful exploitation of the vulnerability? Can cognitive psychological studies (analogous to those conducted on human mathematical cognition) improve our understanding of what psychological “insight” to complex problems of exploit development look like, to inspire better computational models for exploit development?
  • In theory, where would human expert security researchers’ performance most improve if they had far greater pattern-matching ability, or dramatically greater volume of experience with examples of software flaws? What are the greatest limitations to present-day large language models that prevent them from exceeding human experts’ capabilities?

7 — Conclusions

The field of Ethical AI has grown dramatically in recent years. Despite considerable and admirable efforts related to AI fairness, transparency, accountability, and other AI ethics topics, this field has often overlooked or under-scoped security, or failed to focus on it at a sufficient level of granularity for it to be properly addressed in real-world systems. Security is critical to ethical artificial intelligence not because it is a parallel property that we should seek alongside things like fairness, but because it is a prerequisite to it. Specifically, security is a prerequisite to ethical artificial intelligence because you cannot make meaningful guarantees about a system that you do not control.

Large language models (LMs) in particular evoke important security considerations, the beginning of which I’ve begun to unravel in this article. Due to specific properties of LMs, we have good reasons to seek to understand their security implications through (ethical, transparent, and consensual) offensive security research. Future work in this space will ideally address the questions presented in the above section, An Offensive Security Research Agenda for Large Language Models, and build upon them in directions I’ve undoubtedly missed in this initial blog post.

In particular, we should seek to understand the potential capacity of GPT-3 and other language models of similar scale and performance in terms of their ability to generate code, detect insecure code, generate arbitrary code, and even their potential ability to generate working exploit code. While no demonstration of this capability has yet been shown in either experimental or production contexts, there are strong reasons to believe it may be possible, and therefore it is deeply worthy of further study.

8 — Note from the Author

As this work is both early and speculative, I particularly invite constructive corrections or collaborative conversation related to this post to my email address – my first name dot lastname at company domain.

9 — References & Further Reading

[1] Bengio, Y. (2008). Neural net language models. http://www.scholarpedia.org/article/Neural_net_language_models

[2] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

[3] Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. https://arxiv.org/pdf/2005.14165.pdf

[4] https://lambdalabs.com/blog/demystifying-gpt-3/

[5] https://www.fullstackpython.com/gpt-3.html 

[6] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? https://dl.acm.org/doi/10.1145/3442188.3445922

[7] Shoeybi, M., Patwary, M., Puri, R., et al. (2019). Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism. https://arxiv.org/abs/1909.08053

[8] Rosset, C. (2020). Turing-NLG: A 17-billion-parameter language model by Microsoft. https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/ 

[9] Lepikhin, D., Lee, H., Xu, Y., et al. (2020). GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. https://arxiv.org/abs/2006.16668 

[10] Fedus, W., Zoph, B., Shazeer, N. (2021). Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity https://arxiv.org/abs/2101.03961 

[11] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. https://arxiv.org/abs/1907.11692

[12] OpenAI GPT-3 Model Card. https://github.com/openai/gpt-3/blob/master/model-card.md 

[13] Paperswithcode: About WebText Dataset. https://paperswithcode.com/dataset/webtext

[14] CommonCrawl dataset. https://commoncrawl.org/the-data/

[15] Carlini, N., Tramer, F., Wallace, E., et al. (2020). Extracting Training Data from Large Language Models. https://arxiv.org/abs/2012.07805 

[16] OpenAI (2021). DALL-E: Creating Images from Text. https://openai.com/blog/dall-e/

[17] https://twitter.com/sharifshameem/status/1282676454690451457

[18] https://twitter.com/mattshumer_/status/1287125015528341506?s=20 

[19] https://twitter.com/parthi_logan/status/1286818567631982593?s=20 

[20] https://twitter.com/Antonio_GomezM/status/1287969287110443008 

[21] https://twitter.com/FaraazNishtar/status/1285934622891667457?s=20 

[22] https://twitter.com/harlandduman/status/1282132804034150400?s=20

[23] https://twitter.com/harlandduman/status/1282198829136097281?s=20 

[24] Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H., Kaplan, J., et al. (2021). Evaluating Large Language Models Trained on Code. https://arxiv.org/pdf/2107.03374.pdf

[25] OpenAI API Examples. https://beta.openai.com/examples

[26] GitHub Copilot. https://copilot.github.com/

[27] OpenAI human-eval Codex benchmarking suite https://github.com/openai/human-eval

[28] OpenAI (2021). Codex. https://openai.com/blog/openai-codex/

[29] Fernick, J., & Robinson, C. (2021). Securing Open Source Software – End-to-End, at Massive Scale, Together. Black Hat USA 2021. https://www.blackhat.com/us-21/briefings/schedule/#securing-open-source-software—end-to-end-at-massive-scale-together–23140

[30] Goh, G., Cammarata, N., Voss, C., Carter, S., Petrov, M., Schubert, L., Radford, A., & Olah, C. (2021). Multimodal Neurons in Artificial Neural Networks. https://distill.pub/2021/multimodal-neurons/

[31] Fernick, J., & Wheeler, D. A. (2021). Keynote: Securing Open Source Software. Linux Foundation Member Summit 2021.

[32] https://arr.am/2020/07/31/gpt-3-using-fiction-to-demonstrate-how-prompts-impact-output-quality/

[33] Avgerinos, T., Cha, S. K., Rebert, A., Schwartz, E. J., Woo, M., I & Brumley, D. (2014). Automated Exploit Generation. Communications of the ACM 57(2), 74-84. https://cacm.acm.org/magazines/2014/2/171687-automatic-exploit-generation/fulltext

Exploring the Security & Privacy of Canada’s Digital Proof of Vaccination Programs

31 December 2021 at 19:17

by Drew Wade, Emily Liu, and Siddarth Adukia

TL; DR

We studied a range of Canadian provinces’ proof-of-vaccination apps to analyze their associated security and privacy properties. In particular, building on prior work in which some of us created an assessment framework for evaluating the security & privacy of vaccine passports, in this post, we discuss our exploration of the Verify Ontario app in depth to understand its’ ability to correctly reject forged and malformed vaccine credentials, to avoid collecting unnecessary private data, and to manage data using security best-practices.

Canada’s provincial and territorial Proofs of Vaccination are:

  • based in a sound standard that minimizes the inclusion of personal information
  • collected from online sources that securely verify the requestor’s identity
  • protected against tampering by strong cryptographic cyphers

The Verify Ontario application appears to:

  • reliably reject forged and malformed Proofs of Vaccination
  • manage data securely
  • avoid collecting unnecessary private data
  • only temporarily store and transmit non-private analytics data to Google

Introduction

With the requirements for Canadian businesses to verify COVID-19 vaccination status in many settings, and with use of the provincial and territorial proofs of vaccination for international travel, we are left with the question of how well our privacy and proof authenticity is being protected as we show proof of vaccination to businesses and governments at home and around the world. These concerns are most poignant now that Ontario is requiring QR codes to be the means of verifying vaccination status in the face of rising Omicron numbers.

Our goal in this blog post is to provide an overview of the digital proof of vaccination efforts across Canada and to explore in greater technical detail Ontario’s digital proof of vaccination and its proof verification application. We examined the processes for collecting proofs of vaccination, available samples of the proofs for several provinces and territories, and the iOS and Android mobile applications for verifying proofs in Ontario. The Ontario verifier app was reviewed for security and privacy concerns in its handling of network traffic and locally stored data, and for its response to forged and malformed QR codes.

Individual provinces and territories provide greater or lesser protections in claiming a proof of vaccination, largely relying on provincial or territorial health card numbers. Given the major healthcare data breaches in recent years, both Ontario and Newfoundland and Labrador make the notable requirement for a security code or expiry date from a valid health card. These government sites do not appear to be particularly vulnerable to brute-force requests for proofs of vaccination or to manipulation of the government e-health information systems behind them, though our analysis mainly focused on non-server components. No highly-specific or personally-identifying information was found incorporated into pre-populated portions of the site used to verify identity. 

The now pan-Canadian use of the SMART Health Card standard maintains privacy while providing a reliable means of verifying vaccination status. Because the data is digitally signed, a Verifier App only needs to check the signature to confirm that the encoded data is valid and does not need to make connections out to healthcare databases. This limits the opportunities for data exposure and compromise of the provincial or territorial healthcare information systems. Quebec could stand to do better with its information exposure; the inclusion of sex/gender information and the name of the vaccination clinic are not necessary for proof of vaccination.

Most provinces and territories have produced their own verifier applications, able to verify the signed data’s integrity, to interpret a vaccination status from that data, and to display basic identification information to be confirmed against government-issued photo identification. Credentials that only display a vaccination status and name offer a lower degree of assurance that the vaccine credential and government-issued ID belong to the same individual. The Alberta and Ontario verifier apps additionally display the date of birth, decreasing the likelihood of identity spoofing. 

Looking specifically at Ontario, the Proof collection process makes use of multiple stages of identification and validation to securely provide Proofs for those with valid claim to them. The verifier app does not need to be connected to the Internet to work, and only requires Internet access for: (1) updates to Ontario’s vaccination rules and (2) to public keys for other provinces and territories. The app does periodically send analytics data to Google Analytics, but only appears to send basic anonymized device information and non-sensitive QR scan data (e.g. number of positive scans). Importantly, the app correctly failed to validate unsigned, malformed, and forged QR codes, verifying the QR codes based on secure digital signature by the Ontario Ministry of Health. While it is trivial to create your own QR code, it would be near impossible to sign the data in a way that will trick the verifier application without access to the private keys used by the government healthcare bodies issuing valid proofs of vaccination.

Disclaimer

No testing was performed against provincial and territorial vaccination and e-health websites, and only passive observations on the public portion of these sites were made for interactions to receive a proof of vaccination. 

Terminology

Proof (AKA “vaccine passport”, “vaccine certificate”)

a means of verifiably and reliably demonstrating that someone has received one or more COVID-19 vaccinations. This may refer to a variety of cards, documents, websites, and print or digital codes, but in the Canadian context has predominantly come to mean the vaccination receipts and QR codes issued by provincial and territorial health authorities.

Credential App 

a dedicated (typically mobile) application for storing and presenting a Proof for verification at venues required to confirm vaccination status prior to admittance (e.g., gyms, bars, offices, concert halls, etc.). This can be construed to include Apple’s Wallet application, which now stores vaccination Proof QR codes, but some provinces have their own Credential App.

Verifier App

a dedicated (typically mobile) application for reading and validating the authenticity of a Proof. In the Canadian context this is a QR code reader that can verify the integrity of the data presented to confirm its source and authenticity, as well as assess the encoded vaccination types and dates for conformance to provincial regulations.

Evolution of Vaccination Proofs in Canada

In large part due to the provincial and territorial responsibility for healthcare, Canada has taken a province-specific approach to developing vaccine Proofs, Credential Apps, and Verifier Apps. With the mass inoculation of Canadians in the spring of 2021, there became a steadily growing need for a means to demonstrate vaccination status to employers in essential services and other venues hoping to reopen their businesses safely. Beginning in May 2021, Quebec began providing a QR code as an electronic Proof for its citizens based on the SMART Health Card standard. Manitoba followed suit in August, releasing a QR code of their own that linked to the provincial e-health website rather than encoding vaccination data directly to the QR code. British Columbia released a QR code, also using the SMART standard in September, and the remaining provinces and territories have released a variety of SMART-based QR codes, along with Credential and Verifier Apps, to be used in their reopening strategies. At the time of writing, Canada has adopted the SMART standard across the country, with Manitoba issuing a new SMART-based Pan-Canadian Vaccine Credential (PVC) version, to facilitate their use as Canadian proofs of vaccination for international travel. 

SMART Health Card (SHC) Standard

The SMART Health Card standard is now in use across Canada as the basis for demonstrating proof of vaccination in Canadian businesses and for international travel. The SMART standard was designed with privacy in mind, and the standard is intended only to encode:

  •         Your legal name and date of birth
  •         Your clinical information
  •         Tests: date, manufacturer, and result
  •         Vaccinations: type, date, and location

and aligning with the principles of data minimization, should not contain other personal information such as:

  •         Your phone number
  •         Your address
  •         Your government-issued identifier 
  •         Any other health information

A SMART Health Card encodes this information in several ways:

  •         the data is recorded in a structured format (JSON)
  •         the data is digitally signed to demonstrate it has not been tampered with
  •         the signed data is converted to a string of numbers (e.g. shc://235961…884378)
  •         the SHC data is converted to a QR code that can be read by any QR code reader

Because the data is digitally signed, a Verifier App only needs to check the signature to confirm that the encoded data is valid, and does not need to make connections out to healthcare databases. This limits the opportunities for data exposure and compromise of the provincial or territorial healthcare information systems. The SMART Health Card QR code, however, does not store any other individuating information. In order to verify that the data presented in the QR code belongs to the person presenting it, it is still necessary to show additional photo identification, such as a driver’s license or passport, for comparison to the basic name and birthdate information in the QR code data.

Verifier Applications

In order to access the data encoded in a SMART QR code, most people will use a verifier application, which takes the SHC string of numbers and reproduces the vaccination data and digital signature. At this point, most applications will check the structured vaccination data and, depending on the standards of vaccination required (e.g., 2+ doses of Pfizer or Moderna, or 1 AstraZeneca and 1+ Pfizer or Moderna, or 3+ other COVID-19 vaccines, etc.), return a pass/fail indication for those standards. Most provinces and territories have produced their own verifier applications, able to verify the signed data’s integrity, to interpret a vaccination status from that data, and to display basic identification information to be confirmed against government-issued photo identification. 

Not all provinces and territories have their own Verifier Apps and, at various stages of release of QR codes, third-party applications have been recommended with mixed results. The Northwest Territories, for example, has no Verifier App of its own and is recommending The Commons Project’s SMART Health Card Verifier App. The Commons Project is a Rockefeller Foundation-supported non-profit public trust that “builds and operates digital platforms and services for the common good“. While no major issues have arisen from its use, the same cannot be said for other third-party applications like Portpass, recommended by the Calgary Sports and Entertainment Corporation for vaccination status verification at sports arenas in Calgary, which suffered from poor validation in storing and verifying vaccination data.

Canada’s Proofs of Vaccination

For each of the provincial and territorial proofs, we examined each step from the publicly-accessible portions of the process of collecting a Proof through to verification of the Proof, as best we were able to without access to valid Proofs for every province and territory. We begin by providing a review of this information, as it is the most readily understandable to a wide non-technical audience and not limited to a single geographic region of the country. Because one or more of the authors are residents of Ontario and have valid claims to Proofs for Ontario, we were also able to scrutinise the Ontario Proof collection, presentation, and verification processes in greater detail. A more technical and detailed exploration of that process follows the coast-to-coast-to-coast overview of vaccination Proofs in Canada.

Our analysis is loosely modelled on the earlier threat model on vaccine credentials we published on the NCC Group Research blog.

Collecting a Proof

Privacy concerns exist immediately with the process for collecting a Proof; the benefit of proof of vaccination would surely be outweighed by an insecure collection process that exposed sensitive personal and healthcare data. To gauge the security of the issuing site, we accessed the public portions of the provincial and territorial proof of vaccination websites to answer the following questions:

  • How is the information request formatted (free-form text/multiple-choice/calendar/other)?
  • What information is required to verify identity?
  • How is the information transmitted between the browser/application and healthcare record system?
  • Is it likely that an attack process (e.g., brute-force, open-source intelligence) could collect a Proof for someone else?

For the most part, the information is provided via free-form text boxes and calendar pop-ups or drop-down menus. No highly-specific or personally-identifying information is incorporated into pre-populated drop-down menus to verify identity. In a handful of cases, information other than dates is provided using multiple select buttons and drop-down menus. These are used for sex/gender questions and for the commercial names of vaccines. Several sites also used checkboxes to opt out of items that were not applicable (see Quebec info), to confirm completeness and validity of provided information, and for reCAPTCHA confirmations.

For Manitoba, New Brunswick, and Saskatchewan, the collection process is completed within the provincial e-health website, which could not be accessed or tested within the scope of this research. In all three cases, an email and password are required to access the site and the security of the Proofs contained within hinge on the basic security protections applied to the login page. The Yukon Territory process was also incompletely reviewed, as personally-identifying information is provided in stages, with validation of one set required before more is requested and provided; we will look only at the publicly-accessible request here.

The most common pieces of information requested were provincial or territorial health card numbers, names, and dates of birth (see Table 1 below – Identity verification info). While one’s health card number is likely kept private, names and dates of birth are often very easily accessed through OSINT (Open-Source INTelligence – e.g. social media, personal websites, etc). Other requested information that is relatively easy to collect through OSINT includes dates of vaccination (Month/Year for AB, NT; Day/Month/Year for BC, QC), email, postal codes, sex or gender, and the vaccine brand received. The sex and gender questions, too, are very limited (Male/Female “gender” for QC; Male/Female/Other “sex as written on your health card” for YT). The commercial vaccine name options varied widely, from only three options for the Northwest Territories to twenty-three options for Quebec. Several provinces required additional information from the user’s health card, including the security code number, expiry, and version code. Quebec also requests parental names (or confirmation that their identity is unknown), provides an option for confirmation of no insurance (health card) number, and has an option for confirmation of exemption from vaccination. Only four provinces used reCAPTCHAs, but these were limited to an “I am not a robot” checkbox.

Table 1: Identity Verification Information by Province

Testing the protections on the HTTPS-based web forms and the back-end APIs that service the sites was outside the scope of this research, so we will focus on the nature of the information provided to assess the likelihood of an attacker gaining access to Proofs for which they have no valid claim. As mentioned earlier, a great deal of information requested for claiming a Proof is relatively easy to access through OSINT methods. People regularly share their email addresses, their ages and birthdays, their home addresses or neighbourhoods, their sex or gender, and their parents’ names, on social media, job site profiles, and other websites that display this data publicly or have leaked private data at one time or another. In the excitement of finally receiving a vaccination, and from the desire to inspire others to get theirs, many people posted to social media both the date and brand of vaccination they received. 

As a result of all of this publicly-accessible information being used for identity verification in the Proof collection process, most of the efforts at verifying the identity of the individual requesting a Proof rely on knowledge of a provincial or territorial health card number. While Canadians tend to be fairly guarded about that piece of information in their day-to-day lives, it is shared with a variety of healthcare providers. Between the major healthcare data breach at LifeLabs in 2019 and the ongoing targeting of hospitals in ransomware attacks, it is not unlikely that health card numbers and their associated names have been available to attackers seeking to mine more personal information, to profit from schemes to circumvent public health measures, and to disrupt vaccination and proof of vaccination efforts. As a result, the addition of health card expiry and security code features for identity verification become very important. This is particularly relevant for Ontario and British Columbia, which were most heavily affected by the LifeLabs data breach, but at time of writing, only Ontario has implemented these additional identifiers in their Proof collection process.

Other notable exceptions in the Proof request process were Nova Scotia, which required only the email provided at time of vaccination and the health card number, but which sends the Proof to the registered email address; Prince Edward Island, which has an automated phone option that will send a 6-digit verification code to proceed by text or automated call to the phone number on file; and Nunavut, for which Proofs can only be obtained in person at local health centres, Iqaluit Public Health, or Qikiqtani General Hospital.

The Proof

Once a Proof has been claimed, further privacy concerns arise in the form of the data encoded in that Proof. Where possible, we collected sample QR codes from government websites, requested examples or overviews of QR data from colleagues, and examined our own QR codes to understand what information is potentially shared in each verification transaction. We were specifically interested to know:            

  • How is the information encoded?
  • How is the information integrity maintained?
  • What information is encoded?
  • What information is advertised as being encoded? 
  • Can this information be misused?

As all of the provinces and territories of Canada have adopted the SMART Health Card standard, the question of how the encoding is done is relatively simple, as discussed earlier. Based on the QR codes we viewed directly and with the assistance of colleagues (BC, ON, QC, SK, YT), the structured vaccination data is signed using a strong cryptographic system (ECDSA with curve P-256). The reliability of this digitally-signed data will depend on the ability of the government healthcare information systems to safeguard the private keys used to sign the data. The exception to the SMART-based QR codes is the original Manitoba QR code, now for use only within Manitoba, which encoded only a link to the Manitoba immunization card website and a unique 32-character ID number. A username and password login is still required to access the vaccination status information.

For the most part, use of the SMART Health Card standard limited the personally-identifying information encoded in the QR codes we share (see Fig. 1 below – sample SMART data). The holder’s full name and date of birth are both required for validation against photo ID, and in most cases the remaining data is limited to vaccine types, lot numbers, and vaccination dates. There is a valid reason for keeping the lot numbers in there, in case it later turns out that a lot was bad or a placebo or there was some other reason not to treat that batch as a successful inoculation. The Quebec QR code, however, also encodes “gender” (male or female) and the name of the vaccination clinic where the vaccination was received. The other provinces and territories reviewed have used the location field more generally to note the province or territory of vaccination instead. This “gender” and geolocation information is not necessary for identification or for verification of vaccination.

Figure 1: Sample SMART Health Card Data for Saskatchewan

Verifying a Proof

With a Proof in hand – and hopefully not containing too much sensitive personal information – the next important point of contact that represents a potential privacy concern is the application used to verify the Proof. For Proof verification apps, we were concerned with the following questions

  • How is information in the Proof transmitted to the Verifier?
  • How is the integrity of the Proof verified?
  • What information is used by the Verifier to display a result?
  • What information is displayed in the Verifier?
  • Is the source code for the Verifier publicly accessible?

Not every province and territory has produced a Verifier App; Nunavut and New Brunswick are directing their citizens to their government e-health websites, while the Northwest Territories are recommending The Commons Project’s SMART Health Card Verifier app. The provinces and territory that do, however, are making use of the same SMART standard and displaying very similar information. In all of the Verifier Apps, the information is transmitted via the device’s camera to scan the QR code. Android devices require an additional dedicated app (such as the Verifier App) to parse QR codes, while iOS/iPadOS devices are natively capable of reading QR codes from the Camera app. Without a Verifier App, an Apple device reading the QR code will direct the user to the Health app, which can decode and parse SMART Health Card data; the exception being Manitoba’s URL-based QR which is directed to the device’s default browser. Once the QR code is scanned, the SHC data is decoded, and the signature should be checked to verify that the data has not been tampered with since it was signed by the issuing government healthcare information system. The app requires the public key of the cryptographic key-pair used to sign the data to verify the digital signature, allowing the verification to happen offline and reduce the attack surface of the government healthcare information systems. With no private data being sent to or from the device by the Verifier App, there is also no opportunity for an attacker to listen in on the communication. 

While a detailed review of the function of all of the available provincial and territorial Verifier Apps should be performed in the future, it is likely that they function similarly, given that they must work with nearly the same set of SMART Health Card data to confirm vaccination status. This requires the app to check the numbers and types of vaccinations and to compare the vaccination dates to ensure the requisite number of accepted vaccinations have occurred with the required minimum interval between vaccinations. The apps consistently display three items – the holder’s first and last names and the computed vaccination status (pass/fail/error) – but only Alberta and Ontario additionally display the holder’s date of birth and only British Columbia displays the signature date of the Proof. Date of birth, along with the holder’s name, is useful for comparison to government-issued photo ID to verify that the presented credential belongs to the holder. It is unclear what the purpose is in displaying the date that the proof was issued.

An additional concern around Verifier Apps, too, is their accessibility, particularly in regards to the operating system (OS) versions that support them. The minimum OS versions for iOS/iPadOS range from 8.0+ (NS, PE) to 13.0+ (AB), with most requiring iOS/iPadOS 11.0+ (BC, NL, ON, QC, SK, YT) and one requiring iOS/iPadOS 12.0+ (MB). The minimum OS versions for Android devices range from 5.0+ (MB, ON) to 8.1+ (BC, NL, YT), with most requiring Android 8.0+ (AB, QC, SK) and two requiring Android 6.0+ (NS, PE). The requirement for iOS 13 or greater for the Alberta Verifier App necessarily reduces its accessibility to iPhone 6S and later models and to 5th generation iPads, 2nd generation iPad Airs, 4th generation iPad Minis, and iPad Pros.

For ease of reference, the currently available Verifier Apps are listed here by province and territory:

Ontario’s Proof of Vaccination and Verifier App

As the authors of this post included Ontarians with valid claim to Proofs, we were able to look closer at the Proof collection process and the iOS and Android applications for its verification. We will also discuss the process of decoding SMART-based Proofs, covered only in brief above. The analysis below goes into a few technical details; if you’re more interested in the results themselves, jump to the Conclusion and Future Directions sections at the end.

Collecting an Ontario Proof

In addition to examining the requirements for verifying one’s identity to claim a Proof, we looked at the traffic generated during the collection process. Again, as this is a governmental site for which we have no active testing permissions, modifying proxied data during the collection process was not within scope of this research.

By navigating to the https://covid19.ontariohealth.ca website to collect a proof, a GET request to the root of that domain retrieves the main COVID-19 vaccination portal page and sets a covid-portal cookie, the content of which is encrypted. An X-Azure-Ref header was also noted in the response, indicating a service running in Microsoft Azure. At this point, Google tracking pixel or Google Analytics data is also sent to the stats.g.doubleclick.net domain, followed by a GET request to http://www.gstatic.com for a HTTP code 204 contentless response. 

After confirming one has read and agreed to the terms of use to download an enhanced vaccine certificate (or book a vaccine appointment), the site makes a POST to the covid19.ontariohealth.ca domain’s /app-home endpoint including a CSRF token and a viewId parameter in the request’s body, along with an empty accountId parameter and an acceptedTerm1 parameter set to True. This viewId is referenced throughout the Proof collection process, but is not fixed for a particular user – only for the session. In response, the /app-identity endpoint returns the same covid-portal cookie as previously noted along with ASLBSA and ASLBSACORS cookies, further indicating the use of services (load balancing in this case) in Azure. The site then redirects to the /app-identity endpoint and provides the viewId parameter in the URL.

The resulting GET request to the covid19.ontariohealth.ca domain’s /app-identity endpoint results in another redirection, this time to the Ontario Ministry of Health’s ontariomoh.queue-it.net domain, a virtual waiting room platform to queue requests to the Ministry healthcare information servers. This, in turn, produces a GET request to the ontariomoh.queue-it.net domain with a series of parameters in the URL to identify the type of request that will be queued (c=ontariomoh e=covid19mohprod ver=v3-javascript-3.6.3 cver=101 man=mohprod l=CovidPortal) and includes the source of the request with its viewId (t=https%3A%2F%2Fcovid19.ontariohealth.ca%3A443%2Fapp-identity%3FviewId%3D73T382U4CWFP).

The response to the queue-it.net request sets a series of “Queue-it” cookies, of which the queueittoken is provided in the URL of a GET request once again to the covid19.ontariohealth.ca domain’s /app-identity endpoint. The response to this request sets a QueueITAccepted cookie value and redirects yet again to a GET request for the /app-identity endpoint, this time explicitly to port 443 of the webserver. With these cookies all set, the request returns the Proof collection page. 

At this stage, the user can enter the identity verification data discussed previously, and it is sent within the body of a POST request to the /app-identity endpoint along with the CRSF token and viewId as follows: 

            _csrf=0ZLNlCVO-3q_jRKx1lLReJzhXAcYduKLYoFg&viewId=73T382U4CWFP&hcn=1234-567-890&vcode=QQ&scn=QQ3456789&dob=2000-01-01&postal=L6N+2F2

If the submitted data does not match a valid user (as in the fake credential data provided above), the response redirects to the /app-identity endpoint to repeat the identity verification data entry step. If the submitted data does match a valid user, the response redirects to the /app-menu endpoint, including the viewId parameter in the URL.

Following a successful submission of valid identity data, the response redirects to the ontariomoh.queue-it.net domain and the resulting GET request sets a Queue-it-token-v3 cookie, which will expire after 3 minutes. The site then makes a GET request to the /app-menu endpoint which includes the queueittoken cookie in the URL and the response returns the QueueITAccepted cookie before redirecting to /app-menu once again. This time, the GET request returns the page for collecting a Proof (or booking a vaccination). 

Once the user selects the option to collect their Proof, the site triggers a GET request to the covid19.ontariohealth.ca domain’s /vaccine-receipt endpoint. The response to this sets a new Issue Time and Hash value for the QueueITAccepted token and returns a page that displays the user’s vaccination history and provides a link to download the vaccine certificate Proof.

Clicking on the download option, sends a GET request to the covid19.ontariohealth.ca domain’s /vaccine-credential endpoint and specifies the format in a URL format parameter set to “shc”. The response to this request sends the Proof. 

Some minor tampering with the viewId parameter in the URL was attempted to confirm its security, and found that altering the viewId parameter before downloading the Proof certificate results in a return to the start of the whole collection process, while altering the viewId parameter after downloading the Proof certificate resulted in an error:

Sorry, something went wrong. You have tried too many times to download the certificate, and your access has been locked. Please try again after midnight.

The Ontario Proof

As discussed above, the vaccine passport is based on the SMART Health Card framework. The framework begins with JSON data containing the person’s name, date of birth, and vaccine details (such as type, date, and lot of vaccination). It is then minified and serialized using compact JSON Web Signature (JWS) Serialization (see here). Then it is digitally signed with the issuer’s cryptographic private key, in this case the Ontario Ministry of Health. 

The resulting JWS is URL-safe base64 encoded and converted to ASCII numerical data. All of the ASCII values are then shifted down by 45 to ensure that each of the characters is represented by a two-digit number pair and appended to the shc:/ prefix to indicate SMART Health Card data. To examine SMART QR-encoded data, we reversed that process in a Python script that accepts QR images and returns the SHC, JWS, and vaccination data.

The rest of the encoding process is simply taking the SHC data and creating a QR code from it. Since the QR code simply contains an SHC code, which can be easily decoded into the original JSON data, the JSON data is not private and should contain only the bare minimum of personal information. 

Verifying an Ontario Proof

The Verify Ontario app is a mobile application used to verify Proof of Vaccination QR codes created by the Ontario Ministry of Health and those of the other provinces and territories of Canada. Businesses and other establishments wanting or needing to verify vaccination status can download the Verifier App from the Apple App Store or the Google Play Store to scan the QR codes and see if they’re valid and meet the current vaccination requirements in Ontario. 

The Ontario government has also generously made the code to the app public (https://github.com/ongov/OpenVerify). The Verifier App contains a minimal few components: a QR scanner, an About and Settings page, and a response description page. The app doesn’t need to be connected to the Internet at all times in order to work. It only needs an Internet connection in order perform updates, particularly for the latest vaccination rules and cryptographic public keys. 

On start-up, the app sends compressed protobuf data (Google’s structured data serialisation format) out to the firebaselogging-pa.googleapis.com/v1/firelog/legacy/batchlog endpoint to initialise Google Firebase Analytics for this session. We see intermittent communications to Google’s app-measurement.com/a endpoint for Firebase event data transmission, also in the compressed protobuf format (see more on Google’s protobuf data below).

The app then makes GET requests to files.ontario.ca/apps/verify/ for two files – minimumVersion.json and verifyRulesetON.json. The request for minimumVersion.json returns the “minimumMandatoryVersion” information (currently 1.0) and an “effectiveDate” (currently empty). Unlike the Proof collection service, based in Azure, HTTP headers indicate this resonse is sent via AWS’ CloudFront content delivery network. The request for verifyRulesetON.json returns:

  • name, date, and terms of use information
  • ruleset for number and brand of vaccines and days since last dose to declare “Immunization completed”
  • cryptographic public keys for Ontario and other provinces and territories to verify Proofs from them
  • repeat of minimumMandatoryVersion (currently 1.0) and an effectiveDate (currently empty) data

At this point we sought to understand the possibility of a malicious attacker being able to conduct a Denial-of-Service attack on a business or event site via a Person-in-the-Middle interception of the Verifier Apps’ server communication on start-up to manipulate the signature public keys the app is receiving in the verifyRulesetON file. Starting up our own copy of the Verifier App on our device, we attempted an alteration to the copy of the Ontario public key our app used when that file is transmitted, but a valid Ontario QR code still verified successfully. It seems the developers of the app mitigated against this type of attack: any new rulesets pulled to the app must also pass a signature check against the public key hardcoded to the app before altering the Proof validation keys.

We were also interested to test the outcomes of presenting invalid Proofs to the app; that is, how does the Verifier App respond to unsigned, malformed, and manipulated data? To create test cases, we used the playbooks available here. To understand if a QR code can be manipulated or forged and accepted by the app, we generated four basic test cases:

  1. From a decoded valid Proof, create an unsigned version, re-encode it to SHC data, and make a QR from it.
  2. From a valid SHC string, add numbers to the beginning and/or the end, and make a QR from it.
  3. From a decoded valid Proof, change the date of birth, re-encode it to SHC data, and make a QR from it.
  4. From a decoded valid Proof, create a version signed with keys found in the testing folders of the open-source code repo, re-encode it to SHC data, and make a QR from it.

In each case, the app correctly failed to validate the QR code and displayed the yellow Error screen. If the data is signed properly and conforms to the vaccine verification rules found in verifyRulesetON.json, then we would expect to see it display the green Success response. If it isn’t signed properly, it will only give the yellow Error response. As limited QR samples were available and the issuer’s private keys were not, it is assumed that the red Fail response will be displayed if it was correctly signed but did not meet the vaccination rules. It appears that the app is verifying the QR codes bases on how they’re digitally signed, and in order to forge a valid QR code, an attacker would also need to acquire the issuer’s cryptographic private key. This means that it one cannot forge “valid” QR codes unless the provincial health ministry cryptographically signs them (or their cryptographic signing is itself compromised), making forgery of valid QR codes significantly harder than simply generating the OR code itself.

Finally, we looked at the privacy posture of the app, and as we noted earlier, once QR codes are scanned by the Verifier app, calls are made intermittently to Google’s app-measurement.com domain. First, a GET request is made to the /config/app/1:1007566906072:ios:c2fa5ab4f9dd1582c42dad endpoint (or its Android equivalent). The endpoint itself designates the OS’ version of the Verify Ontario app within Google Analytics. The URL also includes an app_instance_id parameter (unique to each user), along with gmp_version, runtime_version, and platform parameters. Once that connection is established, the app makes intermittent POST requests to the /a endpoint, with the compressed protobuf analytics data in the body.

The protobuf data can be decompressed and decoded. We used the tool Burp Suite to intercept data, and Burp has its own protobuf decoder. Other protobuf decoders also exist (such as https://github.com/nccgroup/blackboxprotobuf). We made use of a manual process which exports the POST requests in Burp to HTTP .trace files and extracts just the body (remove headers and newline between headers and data) to save as a separate file. This file can be gunzipped (`gunzip – < request_body_file > request_uncompressed.bin`), which should ignore any trailing garbage. The resulting data can be decoded using the protoc tool (`protoc –decode_raw < request_uncompressed.bin > sent_data`). 

Unfortunately, decoding the protobuf is not particularly helpful; the result is pretty cryptic on the whole. Serialising data allows for more efficient transmission, which is the purpose of compressed protobuf, and naturally Google knows what all of the data is intended to deserialise as. It is possible to clearly pick out the occasional item like iOS version and iPhone version and Linux epoch dates (see Figure 2 below – sample protobuf data), but most other items require some additional context. The Verify Ontario app’s privacy page claims that no personally-identifiable information is sent from the app, and that Google Analytics collects:

  •                 default Google Analytics data
  •                 number of scans completed
  •                 date and time of scans
  •                 number of positive, negative, and invalid scans
  •                 number of times:
    •            scanner is open for more than 20 seconds
    •            verified result screen is left inactive for 30 seconds
    •            users are prompted to connect to the internet 
    •            users are prompted to update their version of the app
    •            users click the link to the app store from the version update prompt
    •            flashlight button is clicked while in the app
    •            a link is clicked in the app
  •                 a “masked” IP Address
    •            only a portion of an IP address is collected
    •            used for geolocation of a user         

The analytics source code for the app and the interpretable parts of the protobuf data would seem to bear out this assertion.

Figure 2: Protobuf Data Sample   

A review of the Android app’s storage reveals that the app mainly writes files within the cache storage and the data directory. Overall, the app is storing Google Analytics data and no private encryption keys or Proofs were being stored within the physical device. 

The following are related directories that were discovered on the Android device:

  •             cacheDirectory           /data/data/ca.ontario.verify/cache
  •             codeCacheDirectory       /data/data/ca.ontario.verify/code_cache
  •             externalCacheDirectory   /storage/emulated/0/Android/data/ca.ontario.verify/cache
  •             filesDirectory           /data/data/ca.ontario.verify/files
  •             obbDir                   /storage/emulated/0/Android/obb/ca.ontario.verify
  •             packageCodePath          /data/app/ca.ontario.verify-1/base.apk

Within these directories the contents review was uneventful, with three of the directories being empty (codeCacheDirectory, externalCacheDirectory, obbDir). The other directories contained cached HTTP requests (cacheDirectory) and public keys for Firebase (filesDirectory). The surrounding data directories (filesDirectory and packageCodePath) contain the majority of the library and Google Analytics files: 

            /data/app/ca.ontario.verify-1

            Type       Last Modified            Read  Write  Hidden  Size       Name

  • File       2021-10-18 23:21:09 GMT  True  False  False   121.4 KiB  split_config.xhdpi.apk            
  • File       2021-10-18 23:21:12 GMT  True  False  False   24.4 KiB   split_config.fr.apk
  • File       2021-10-18 23:21:14 GMT  True  False  False   40.4 KiB   split_config.en.apk
  • File       2021-10-18 23:21:18 GMT  True  False  False   4.5 MiB    split_config.armeabi_v7a.apk
  • Directory  2021-10-18 23:21:19 GMT  True  False  False   4.0 KiB    lib
  • File       2021-10-18 23:21:07 GMT  True  False  False   6.8 MiB    base.apk

            /data/data/ca.ontario.verify

            Type       Last Modified            Read  Write  Hidden  Size     Name

  • Directory  2021-10-18 23:21:20 GMT  True  False  False   4.0 KiB  lib
  • Directory  2021-10-25 16:34:09 GMT  True  True   False   4.0 KiB  cache
  • Directory  2021-10-20 02:34:35 GMT  True  True   False   4.0 KiB  lib-3
  • Directory  2021-10-27 14:41:04 GMT  True  True   False   4.0 KiB  files
  • Directory  2021-10-20 02:34:35 GMT  True  True   False   4.0 KiB  lib-2
  • Directory  2021-10-20 02:34:35 GMT  True  True   False   4.0 KiB  lib-1
  • Directory  2021-10-28 17:19:29 GMT  True  True   False   4.0 KiB  shared_prefs
  • Directory  2021-10-20 02:34:35 GMT  True  True   False   4.0 KiB  lib-0
  • Directory  2021-10-20 02:34:35 GMT  True  True   False   4.0 KiB  lib-main
  • Directory  2021-10-20 02:35:01 GMT  True  True   False   4.0 KiB  databases
  • Directory  2021-10-28 16:46:28 GMT  True  True   False   4.0 KiB  app_webview
  • Directory  2021-10-20 02:35:01 GMT  True  True   False   4.0 KiB  app_textures
  • Directory  2021-10-28 16:32:49 GMT  True  True   False   4.0 KiB  code_cache

            /data/data/ca.ontario.verify/shared_prefs

            Type  Last Modified            Read  Write  Hidden  Size     Name

  • File  2021-10-29 15:44:47 GMT  True  True   False   920.0 B com.google.android.gms.measurement.prefs.xml
  • File  2021-10-27 14:41:03 GMT  True  True   False   188.0 B  FirebaseAppHeartBeat.xml
  • File  2021-10-20 02:35:01 GMT  True  True   False   127.0 B  WebViewChromiumPrefs.xml

A SQLite database was used to temporarily store Google Analytics Firebase data. 

            /data/data/ca.ontario.verify/database

            Type  Last Modified            Read  Write  Hidden  Size      Name

  • File  2021-10-28 17:19:55 GMT  True  True   False   36.0 KiB  RKStorage
  • File  2021-10-28 17:19:55 GMT  True  True   False   32.6 KiB  RKStorage-journal
  • File  2021-10-28 17:19:29 GMT  True  True   False   16.0 KiB  google_app_measurement_local.db
  • File  2021-10-28 17:19:29 GMT  True  True   False   8.5 KiB   google_app_measurement_local.db-journal

The following services, activities, and referenced files that were discovered while the app was running are also unconcerning:

            Services running

  • com.google.android.datatransport.runtime.backends.TransportBackendDiscovery
  • com.google.android.datatransport.runtime.scheduling.jobscheduling.JobInfoSchedulerService
  • com.google.android.gms.auth.api.signin.RevocationBoundService
  • com.google.android.gms.measurement.AppMeasurementJobService
  • com.google.android.gms.measurement.AppMeasurementService
  • com.google.firebase.components.ComponentDiscoveryService
  • com.google.mlkit.common.internal.MlKitComponentDiscoveryService

            Files referenced during runtime

  • /data/data/ca.ontario.verify/files/generatefid.lock
  • /data/data/ca.ontario.verify/databases/google_app_measurement_local.db
  • /data/data/ca.ontario.verify/databases/google_app_measurement_local.db-journal
  • /dev/ashmem
  • /data/data/ca.ontario.verify/files/PersistedInstallation.W0RFRkFVT<cut>0MmRhZA.json
  • /data/data/ca.ontario.verify/databases
  • /data/data/ca.ontario.verify/shared_prefs/com.google.android.gms.measurement.prefs.xml
  • /data/app/ca.ontario.verify-1/split_config.xhdpi.apk

            Activities running

  • ca.ontario.verify.MainActivity
  • com.google.android.gms.auth.api.signin.internal.SignInHubActivity
  • com.google.android.gms.common.api.GoogleApiActivity
  • com.proyecto26.inappbrowser.ChromeTabsManagerActivity
  • com.zoontek.rnbootsplash.RNBootSplashActivity

Finally, examination of logging on the device using the logcat tool did not reveal any privacy concerns, either.

Conclusion

Based on our research conducted thus far, Canada’s provincial and territorial Proofs appear:

  • based in a sound standard that minimizes the inclusion of personal information
  • collected from online sources that securely verify the requestor’s identity
  • protected against tampering by strong cryptographic ciphers

The Verify Ontario application appears to:

  • reliably reject forged and malformed Proofs
  • manage data securely
  • avoid collecting unnecessary private data
  • only temporarily store and transmit non-private analytics data to Google

Future Directions

In order to provide information about these Proofs and Apps in a timely fashion – since we recognize their importance in public health measures that are intended to protect us all – we have necessarily limited our scope in this post. The use of the SMART Health Care standard for all Canadian Proofs substantially limits the inclusion of unnecessary information that risks users’ privacy. The Credential Apps that store those Proofs and the Verifier Apps of other provinces and territories, however, do warrant further security and privacy research. 

Several provinces currently offer a Credential App (MB, NL, QC, SK), and others may choose to join suit, and we would particularly like to know how the Proof is handled within device storage and where else the Proof may be sent, stored, or shared. If multiple Proof objects can be stored in the Credential App, how are they distinguished by the user? Will we see open-source code accessible for these apps?

Nearly all of the other provinces and territories have their own Verifier Apps, which is where we expect to see the greatest variability in form and function. In future research, we would like to examine them similarly to how we’ve examined the Ontario Verifier App, to understand what information is being sent, stored, and shared on the device and how the app handles unsigned, manipulated, and malformed data.

Tool Update – ruby-trace: A Low-Level Tracer for Ruby

31 December 2021 at 17:59

We released ruby-trace back in August to coincide with my DEF CON 29 talk on it and parasitic tracing in general. Back then, it supported (c)Ruby 2.6 through 3.0. A few days ago, Ruby 3.1 was released. We have updated ruby-trace to add support for Ruby 3.1 and reorganized our test suite to validate our handling of 3.1 changes across Ruby 2.6 through 3.1.

Background

Back in this post from late 2020, I discussed some of the problems with Ruby’s tracing APIs and tracing the lower-level behaviors of Ruby in general, especially when calls start going into native code and you need to capture arguments and return values. Ruby’s Kernel#set_trace_func and TracePoint API that wraps it are Ruby’s main interface to its built-in tracing capabilities. These are focused around “events” that are emitted by the tracing implementation kicked off by the internal vm_trace() function, which gets called by the trace_* variants of Ruby’s bytecode instructions that simply call it and then jump to the non-trace bytecode instruction handler. When Ruby’s tracing is enabled, it switches bytecode execution to use the trace_* instruction handler for every instruction, ensuring that vm_trace() is called. Additionally, there are a number of EXEC_EVENT_HOOK() macro calls strewn throughout the lower-level Ruby runtime code to emit events for native events such as :c_call/:c_return and thread operations.

However, these events leave much to be desired. Even though it could arguably be modified to provide information on the bytecode instructions being executed, it only provides higher-level information focused mostly on method, class, module, and block entry or returns and doesn’t provide any means to intercept method arguments, let alone native function parameters. And it also provides very limited information on Ruby-to-native transitions, mostly just the Ruby names of native methods. Arguably, this is due to the fact that YARV bytecode is an implementation detail of the current CRuby, but it seems odd that it would not provide an extension of the TracePoint API for additional implementation-specific information, especially given how much bytecode-related functionality is thrown into the CRuby-specific RubyVM module. So I wrote my own…

ruby-trace

ruby-trace is a Frida-based tracer for CRuby, the main Ruby implementation, written in JavaScript. Targeting Ruby on Linux, it instruments Ruby/libruby to dump execution information from Ruby programs. It focuses on extracting all relevant execution information, including method, bytecode instruction, and native function arguments and return values, and additional metadata around control flow, such as branch choices and exception handling. In fact, the “simplest” part of ruby-trace is probably its general hook implementation for Ruby VM opcode handlers, for which the bytecode typically executes in a large while-goto state machine in normal builds. In addition to hooks on the Ruby send, opt_send_without_block, invokesuper, and invokebock opcodes that are primarily used to call things from Ruby code, it additionally hooks a number of other mechanisms internal to Ruby that are used to call Ruby methods and native functions, such as rb_vm_call_cfunc, vm_call_cfunc(_with_frame), rb_vm_call0, and rb_iterate0; and the various Ruby exception handling mechanisms and their native components.

To emit useful output, ruby-trace makes liberal use of the Ruby inspect method to stringify objects, with alternate handling for the critical regions in the VM where Ruby functions cannot safely be dispatched. It will additionally disassemble bytecode objects (e.g. instruction sequence (“ISEQ”)), like methods and blocks, when they are defined (and occasionally generated). It also provides useful human-readable representations for a number of internal values and flags.

Overall, ruby-trace, very deeply instruments the Ruby VM, covering every single one of the 110 Ruby VM opcodes between Ruby 2.6 and 3.1, including several that Ruby itself is incapable of emitting, including vestigial opcodes that likely never actually worked at all.

Design

ruby-trace deeply integrates with Ruby/libruby, not only in the form of its hooks placed throughout, but also in its use of Ruby symbols and C API functions to work with, analyze, and invoke methods on Ruby objects. ruby-trace bundles a large number of Ruby struct definitions for each version and uses a shared interface to abstract them, with version-specific overrides where necessary, enabling it to directly access fields from internal structs and extract call and opcode metadata. The main benefit of this approach is that it is fairly simple to update ruby-trace to support additional versions of Ruby, and doing so does not require maintaining an out-of-tree patchset or unmaintainable fork of Ruby to gain advanced trace output.

Because YARV instruction handlers are not generally compiled as functions with proper preludes and returns, but instead are labeled goto blocks in a state machine, it is not really possible to hook their return, because they generally don’t return at all. Instead, to get opcode results, which are typically placed on the top of the Ruby VM stack, ruby-trace abuses the fact that it hooks all opcode handler entrypoints to unravel the state machine from the inside and place callback functions to pull the returned values before the next instruction is executed.

In general, other than its liberal use of inspect, ruby-trace attempts to avoid performing actions that will cause significant side-effects on execution. As it cannot always glean the full context of a given instruction being executed, ruby-trace often reimplements the logic within opcode handlers to precompute certain information about the instruction being executed based on its register and stack arguments. Additionally, due to the fact that ruby-trace must stringify Ruby objects during critical regions within the VM where Ruby’s method dispatch cannot be used, it implements its own form of Ruby method dispatch parallel to the one internal to Ruby by assembling maps for all inspect and to_s methods by hooking rb_define_method and rb_define_alias. Due to these techniques, ruby-trace, in some sense, acts like a second YARV VM injected into Ruby. Alas, much of the code within ruby-trace exists solely for these kinds of reasons, to prevent Ruby’s own code from crashing itself because Ruby does not like calling Ruby methods during arbitrary junctures within the VM’s execution; much of the work on ruby-trace is focused on squashing instances of memory corruption Ruby constantly does to itself. And trust me, it’s turtles all the way down and all the way up.

Usage

ruby-trace can be installed with sudo npm install -g ruby-trace. To use ruby-trace, add and enable a TracePoint object to a script to trace as shown below, and run the script with ruby-trace -- prepended to the ruby command used to run the script.

demo.rb
require 'bigdecimal'

def trace
  t = TracePoint.new(:call) { |tp| }
  t.enable
  yield
ensure
  t.disable
end

def foo(x)
  case x
  when 'hello'
    'world'
  when 1
    "num"
  when 2147483648
    "bignum"
  when 3.0
    "float"
  when true
    "bool"
  when nil
    "nil"
  when "foo"
    "string"
  when :foo
    "symbol"
  else
    "not found: " + x.to_s
  end
end

trace {
  a = [foo('hello'), foo(1), foo(2.0 + 1.0), foo(BigDecimal("3.0")), foo(:foo), foo("foo"), foo(2147483648)]

  class Symbol
    undef ===
    def ===(*args)
      true
    end
  end
  b = [foo('hello'), foo(1), foo(2.0 + 1.0), foo(BigDecimal("3.0")), foo(:foo), foo("foo"), foo(2147483648)]
  puts [a, b].inspect
}
ruby-trace -- ruby demo.rb
# ruby-trace -- ruby demo.rb
[ruby-trace]{info} vm.ruby_version: 31
[ruby-trace]{info} OPTS: ["OPT_DIRECT_THREADED_CODE","OPT_OPERANDS_UNIFICATION","OPT_INLINE_METHOD_CACHE"]
[6236]{L} >> pop (false)
[6236]{L} >> invokeblock: yield [type:proc, flags:ARGS_SIMPLE] {#<Proc:0x00007f1d6d8fd798 test/demo.rb:34>}
[6236]{L} >> putself (main)
[6236]{L} >> putstring "hello"
[6236]{L} >> opt_send_without_block: (main).foo "hello" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> "hello"
[6236]{L} >> dup ("hello")
[6236]{L} >> dup -> TOPN(0): "hello"
[6236]{L} >>     -> TOPN(1): "hello"
[6236]{L} >> opt_case_dispatch key: "hello" (type:5), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: "hello" (0x94628585850776->0x94628585851376)
[6236]{L} >> pop ("hello")
[6236]{L} >> putstring "world"
[6236]{L} >> leave -> "world"
[6236]{L} >> putself (main)
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> opt_send_without_block: (main).foo 1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 1
[6236]{L} >> dup (1)
[6236]{L} >> dup -> TOPN(0): 1
[6236]{L} >>     -> TOPN(1): 1
[6236]{L} >> opt_case_dispatch key: 1 (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: 1 (0x94628585850776->0x94628585851408)
[6236]{L} >> pop (1)
[6236]{L} >> putstring "num"
[6236]{L} >> leave -> "num"
[6236]{L} >> putself (main)
[6236]{L} >> putobject (2.0)
[6236]{L} >> putobject (1.0)
[6236]{L} >> opt_plus: (1.0) + (2.0)
[6236]{L} >> opt_plus -> 3.0
[6236]{L} >> opt_send_without_block: (main).foo 3.0 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 3.0
[6236]{L} >> dup (3.0)
[6236]{L} >> dup -> TOPN(0): 3.0
[6236]{L} >>     -> TOPN(1): 3.0
[6236]{L} >> opt_case_dispatch key: 3.0 (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: 3 (0x94628585850776->0x94628585851472)
[6236]{L} >> pop (3.0)
[6236]{L} >> putstring "float"
[6236]{L} >> leave -> "float"
[6236]{L} >> putself (main)
[6236]{L} >> putself (main)
[6236]{L} >> putstring "3.0"
[6236]{L} >> opt_send_without_block: (main).BigDecimal "3.0" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6f10, ec->cfp: 0x7f1d6dbe6f10
[6236]{L} >> vm_call_cfunc: (main).BigDecimal // cfunc.func: <unknown>[@0x7f1d6d868560](argc: 1, defined argc: -1); cfunc.invoker: ractor_safe_call_cfunc_m1[@0x7f1d772bba40]
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d868560](0x1, ["3.0"], main)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>) -> #<Thread:0x00007f1d6dab7160 run>
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d868560](0x1, ["3.0"], main) -> 0.3e1
[6236]{L} >> opt_send_without_block: (main).foo 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 0.3e1
[6236]{L} >> dup (0.3e1)
[6236]{L} >> dup -> TOPN(0): 0.3e1
[6236]{L} >>     -> TOPN(1): 0.3e1
[6236]{L} >> opt_case_dispatch key: 0.3e1 (type:12), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: ("hello").=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 0.3e1)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 0.3e1) -> false
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { false }; jump: not taken
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> topn n: 1 [ 1, 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: (1).=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (1).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 0.3e1)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 69882184263540)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 69882184263540) -> 69882184263540
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d89cee8:type_guess=unknown_const>, 1)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>) -> #<Thread:0x00007f1d6dab7160 run>
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d89cee8:type_guess=unknown_const>, 1) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 0.3e1) -> false
[6236]{L} >> branchif +61 (0x56106f3ec818->0x56106f3eca10) { false }; jump: not taken
[6236]{L} >> putobject (2147483648)
[6236]{L} >> topn n: 1 [ 2147483648, 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: (2147483648).=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (2147483648).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 0.3e1)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}}=>true}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}}=>true}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 69882184263540)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 69882184263540) -> 69882184263540
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d89cee8:type_guess=unknown_const>, 2147483648)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>) -> #<Thread:0x00007f1d6dab7160 run>
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d89cee8:type_guess=unknown_const>, 2147483648) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 0.3e1) -> false
[6236]{L} >> branchif +57 (0x56106f3ec858->0x56106f3eca30) { false }; jump: not taken
[6236]{L} >> putobject (3.0)
[6236]{L} >> topn n: 1 [ 3.0, 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: (3.0).=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (3.0).=== // cfunc.func: rb_float_equal[@0x7f1d771acff0](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, 0.3e1)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 69882184263540)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 69882184263540) -> 69882184263540
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d89cee8:type_guess=unknown_const>, 3.0)
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d89cee8:type_guess=unknown_const>, 3.0) -> true
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, 0.3e1) -> true
[6236]{L} >> branchif +53 (0x56106f3ec898->0x56106f3eca50) { true }; jump: taken
[6236]{L} >> pop (0.3e1)
[6236]{L} >> putstring "float"
[6236]{L} >> leave -> "float"
[6236]{L} >> putself (main)
[6236]{L} >> putobject (:foo)
[6236]{L} >> opt_send_without_block: (main).foo :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> :foo
[6236]{L} >> dup (:foo)
[6236]{L} >> dup -> TOPN(0): :foo
[6236]{L} >>     -> TOPN(1): :foo
[6236]{L} >> opt_case_dispatch key: :foo (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: :foo (0x94628585850776->0x94628585851600)
[6236]{L} >> pop (:foo)
[6236]{L} >> putstring "symbol"
[6236]{L} >> leave -> "symbol"
[6236]{L} >> putself (main)
[6236]{L} >> putstring "foo"
[6236]{L} >> opt_send_without_block: (main).foo "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> "foo"
[6236]{L} >> dup ("foo")
[6236]{L} >> dup -> TOPN(0): "foo"
[6236]{L} >>     -> TOPN(1): "foo"
[6236]{L} >> opt_case_dispatch key: "foo" (type:5), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: "foo" (0x94628585850776->0x94628585851568)
[6236]{L} >> pop ("foo")
[6236]{L} >> putstring "string"
[6236]{L} >> leave -> "string"
[6236]{L} >> putself (main)
[6236]{L} >> putobject (2147483648)
[6236]{L} >> opt_send_without_block: (main).foo 2147483648 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 2147483648
[6236]{L} >> dup (2147483648)
[6236]{L} >> dup -> TOPN(0): 2147483648
[6236]{L} >>     -> TOPN(1): 2147483648
[6236]{L} >> opt_case_dispatch key: 2147483648 (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: 2147483648 (0x94628585850776->0x94628585851440)
[6236]{L} >> pop (2147483648)
[6236]{L} >> putstring "bignum"
[6236]{L} >> leave -> "bignum"
[6236]{L} >> newarray num: 7 [ "world", "num", "float", "float", "symbol", "string", "bignum" ]
[6236]{L} >> newarray -> ["world", "num", "float", "float", "symbol", "string", "bignum"]
[6236]{L} >> setlocal_WC_0 [email protected]:0(raw:4)[1/2], level:0, val: (["world", "num", "float", "float", "symbol", "string", "bignum"])
[6236]{L} >> putspecialobject 3 (VM_SPECIAL_OBJECT_CONST_BASE) {Object}
[6236]{L} >> putnil
[6236]{L} >> defineclass id: :Symbol, class_iseq: 0x7f1d6d8e5d50, flags: 0 (VM_DEFINECLASS_TYPE_CLASS), cbase: Object, super: nil
[6236]{L} == disasm: #<ISeq:<class:Symbol>@test/demo.rb:37 (37,2)-(42,5)> (catch: FALSE)
[6236]{L}    0000 putspecialobject                       1                         (  38)[LiCl]
[6236]{L}    0002 putspecialobject                       2
[6236]{L}    0004 putobject                              :===
[6236]{L}    0006 opt_send_without_block                 <calldata!mid:core#undef_method, argc:2, ARGS_SIMPLE>
[6236]{L}    0008 pop
[6236]{L}    0009 definemethod                           :===, ===                 (  39)[Li]
[6236]{L}    0012 putobject                              :===
[6236]{L}    0014 leave                                                            (  42)[En]
[6236]{L} 
[6236]{L}    == disasm: #<ISeq:[email protected]/demo.rb:39 (39,4)-(41,7)> (catch: FALSE)
[6236]{L}    local table (size: 1, argc: 0 [opts: 0, rest: 0, post: 0, block: -1, kw: [email protected], kwrest: -1])
[6236]{L}    [ 1] [email protected]<Rest>
[6236]{L}    0000 putobject                              true                      (  40)[LiCa]
[6236]{L}    0002 leave                                                            (  41)[Re]
[6236]{L} >> putspecialobject 1 (VM_SPECIAL_OBJECT_VMCORE) {BasicObject}
[6236]{L} >> putspecialobject 2 (VM_SPECIAL_OBJECT_CBASE) {Symbol}
[6236]{L} >> putobject (:===)
[6236]{L} >> opt_send_without_block: (BasicObject).core#undef_method(Symbol, :===) [flags:ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (BasicObject).core#undef_method // cfunc.func: m_core_undef_method[@0x7f1d772dd9a0](argc: 2); cfunc.invoker: ractor_safe_call_cfunc_2[@0x7f1d772bba70]
[6236]{L} >> cfunc: m_core_undef_method[@0x7f1d772dd9a0](<uninspectable:0x7f1d6dab18a0:type_guess=unknown_const>, Symbol, :===)
[6236]{L} >> cfunc: m_core_undef_method[@0x7f1d772dd9a0](<uninspectable:0x7f1d6dab18a0:type_guess=unknown_const>, Symbol, :===) -> nil
[6236]{L} >> pop (nil)
[6236]{L} >> definemethod id: :===, iseq: 0x7f1d6d8e5d00
[6236]{L} == disasm: #<ISeq:[email protected]/demo.rb:39 (39,4)-(41,7)> (catch: FALSE)
[6236]{L}    local table (size: 1, argc: 0 [opts: 0, rest: 0, post: 0, block: -1, kw: [email protected], kwrest: -1])
[6236]{L}    [ 1] [email protected]<Rest>
[6236]{L}    0000 putobject                              true                      (  40)[LiCa]
[6236]{L}    0002 leave                                                            (  41)[Re]
[6236]{L} >> putobject (:===)
[6236]{L} >> leave -> :===
[6236]{L} >> pop (:===)
[6236]{L} >> putself (main)
[6236]{L} >> putstring "hello"
[6236]{L} >> opt_send_without_block: (main).foo "hello" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> "hello"
[6236]{L} >> dup ("hello")
[6236]{L} >> dup -> TOPN(0): "hello"
[6236]{L} >>     -> TOPN(1): "hello"
[6236]{L} >> opt_case_dispatch key: "hello" (type:5), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through (:=== redefined)
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", "hello" ] (top->bottom)
[6236]{L} >> topn -> "hello"
[6236]{L} >> opt_send_without_block: ("hello").=== "hello" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", "hello")
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", "hello") -> true
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { true }; jump: taken
[6236]{L} >> pop ("hello")
[6236]{L} >> putstring "world"
[6236]{L} >> leave -> "world"
[6236]{L} >> putself (main)
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> opt_send_without_block: (main).foo 1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 1
[6236]{L} >> dup (1)
[6236]{L} >> dup -> TOPN(0): 1
[6236]{L} >>     -> TOPN(1): 1
[6236]{L} >> opt_case_dispatch key: 1 (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through (:=== redefined)
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", 1 ] (top->bottom)
[6236]{L} >> topn -> 1
[6236]{L} >> opt_send_without_block: ("hello").=== 1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 1)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 1) -> false
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { false }; jump: not taken
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> topn n: 1 [ 1, 1 ] (top->bottom)
[6236]{L} >> topn -> 1
[6236]{L} >> opt_send_without_block: (1).=== 1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (1).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 1)
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 1) -> true
[6236]{L} >> branchif +61 (0x56106f3ec818->0x56106f3eca10) { true }; jump: taken
[6236]{L} >> pop (1)
[6236]{L} >> putstring "num"
[6236]{L} >> leave -> "num"
[6236]{L} >> putself (main)
[6236]{L} >> putobject (2.0)
[6236]{L} >> putobject (1.0)
[6236]{L} >> opt_plus: (1.0) + (2.0)
[6236]{L} >> opt_plus -> 3.0
[6236]{L} >> opt_send_without_block: (main).foo 3.0 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 3.0
[6236]{L} >> dup (3.0)
[6236]{L} >> dup -> TOPN(0): 3.0
[6236]{L} >>     -> TOPN(1): 3.0
[6236]{L} >> opt_case_dispatch key: 3.0 (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through (:=== redefined)
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", 3.0 ] (top->bottom)
[6236]{L} >> topn -> 3.0
[6236]{L} >> opt_send_without_block: ("hello").=== 3.0 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 3.0)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 3.0) -> false
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { false }; jump: not taken
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> topn n: 1 [ 1, 3.0 ] (top->bottom)
[6236]{L} >> topn -> 3.0
[6236]{L} >> opt_send_without_block: (1).=== 3.0 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (1).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 3.0)
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 3.0) -> false
[6236]{L} >> branchif +61 (0x56106f3ec818->0x56106f3eca10) { false }; jump: not taken
[6236]{L} >> putobject (2147483648)
[6236]{L} >> topn n: 1 [ 2147483648, 3.0 ] (top->bottom)
[6236]{L} >> topn -> 3.0
[6236]{L} >> opt_send_without_block: (2147483648).=== 3.0 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (2147483648).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 3.0)
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 3.0) -> false
[6236]{L} >> branchif +57 (0x56106f3ec858->0x56106f3eca30) { false }; jump: not taken
[6236]{L} >> putobject (3.0)
[6236]{L} >> topn n: 1 [ 3.0, 3.0 ] (top->bottom)
[6236]{L} >> topn -> 3.0
[6236]{L} >> opt_send_without_block: (3.0).=== 3.0 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (3.0).=== // cfunc.func: rb_float_equal[@0x7f1d771acff0](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, 3.0)
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, 3.0) -> true
[6236]{L} >> branchif +53 (0x56106f3ec898->0x56106f3eca50) { true }; jump: taken
[6236]{L} >> pop (3.0)
[6236]{L} >> putstring "float"
[6236]{L} >> leave -> "float"
[6236]{L} >> putself (main)
[6236]{L} >> putself (main)
[6236]{L} >> putstring "3.0"
[6236]{L} >> opt_send_without_block: (main).BigDecimal "3.0" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6f10, ec->cfp: 0x7f1d6dbe6f10
[6236]{L} >> vm_call_cfunc: (main).BigDecimal // cfunc.func: <unknown>[@0x7f1d6d868560](argc: 1, defined argc: -1); cfunc.invoker: ractor_safe_call_cfunc_m1[@0x7f1d772bba40]
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d868560](0x1, ["3.0"], main)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>) -> #<Thread:0x00007f1d6dab7160 run>
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d868560](0x1, ["3.0"], main) -> 0.3e1
[6236]{L} >> opt_send_without_block: (main).foo 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 0.3e1
[6236]{L} >> dup (0.3e1)
[6236]{L} >> dup -> TOPN(0): 0.3e1
[6236]{L} >>     -> TOPN(1): 0.3e1
[6236]{L} >> opt_case_dispatch key: 0.3e1 (type:12), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: ("hello").=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 0.3e1)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 0.3e1) -> false
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { false }; jump: not taken
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> topn n: 1 [ 1, 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: (1).=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (1).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 0.3e1)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 69882184280480)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 69882184280480) -> 69882184280480
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d8a5340:type_guess=unknown_const>, 1)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>) -> #<Thread:0x00007f1d6dab7160 run>
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d8a5340:type_guess=unknown_const>, 1) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 0.3e1) -> false
[6236]{L} >> branchif +61 (0x56106f3ec818->0x56106f3eca10) { false }; jump: not taken
[6236]{L} >> putobject (2147483648)
[6236]{L} >> topn n: 1 [ 2147483648, 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: (2147483648).=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (2147483648).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 0.3e1)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 69882184280480)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 69882184280480) -> 69882184280480
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d8a5340:type_guess=unknown_const>, 2147483648)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>)
[6236]{L} >> cfunc: thread_s_current[@0x7f1d7728dc00](<uninspectable:thread_s_current() arg>) -> #<Thread:0x00007f1d6dab7160 run>
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d8a5340:type_guess=unknown_const>, 2147483648) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 0.3e1) -> false
[6236]{L} >> branchif +57 (0x56106f3ec858->0x56106f3eca30) { false }; jump: not taken
[6236]{L} >> putobject (3.0)
[6236]{L} >> topn n: 1 [ 3.0, 0.3e1 ] (top->bottom)
[6236]{L} >> topn -> 0.3e1
[6236]{L} >> opt_send_without_block: (3.0).=== 0.3e1 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (3.0).=== // cfunc.func: rb_float_equal[@0x7f1d771acff0](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, 0.3e1)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 69882184280480)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 69882184280480) -> 69882184280480
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d8a5340:type_guess=unknown_const>, 3.0)
[6236]{L} >> cfunc: <unknown>[@0x7f1d6d86b320](<uninspectable:0x7f1d6d8a5340:type_guess=unknown_const>, 3.0) -> true
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, 0.3e1) -> true
[6236]{L} >> branchif +53 (0x56106f3ec898->0x56106f3eca50) { true }; jump: taken
[6236]{L} >> pop (0.3e1)
[6236]{L} >> putstring "float"
[6236]{L} >> leave -> "float"
[6236]{L} >> putself (main)
[6236]{L} >> putobject (:foo)
[6236]{L} >> opt_send_without_block: (main).foo :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> :foo
[6236]{L} >> dup (:foo)
[6236]{L} >> dup -> TOPN(0): :foo
[6236]{L} >>     -> TOPN(1): :foo
[6236]{L} >> opt_case_dispatch key: :foo (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through (:=== redefined)
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: ("hello").=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", :foo)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", :foo) -> false
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { false }; jump: not taken
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> topn n: 1 [ 1, :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: (1).=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (1).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, :foo)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 1127388)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 1127388) -> 1127388
[6236]{L} >> cfunc: rb_obj_equal[@0x7f1d771b7d40](#<Symbol:0x0000000000dc310c>, 1)
[6236]{L} >> cfunc: rb_obj_equal[@0x7f1d771b7d40](#<Symbol:0x0000000000dc310c>, 1) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, :foo) -> false
[6236]{L} >> branchif +61 (0x56106f3ec818->0x56106f3eca10) { false }; jump: not taken
[6236]{L} >> putobject (2147483648)
[6236]{L} >> topn n: 1 [ 2147483648, :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: (2147483648).=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (2147483648).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, :foo)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 1127388)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 1127388) -> 1127388
[6236]{L} >> cfunc: rb_obj_equal[@0x7f1d771b7d40](#<Symbol:0x0000000000dc310c>, 2147483648)
[6236]{L} >> cfunc: rb_obj_equal[@0x7f1d771b7d40](#<Symbol:0x0000000000dc310c>, 2147483648) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, :foo) -> false
[6236]{L} >> branchif +57 (0x56106f3ec858->0x56106f3eca30) { false }; jump: not taken
[6236]{L} >> putobject (3.0)
[6236]{L} >> topn n: 1 [ 3.0, :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: (3.0).=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (3.0).=== // cfunc.func: rb_float_equal[@0x7f1d771acff0](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, :foo)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 1127388)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 1127388) -> 1127388
[6236]{L} >> cfunc: rb_obj_equal[@0x7f1d771b7d40](#<Symbol:0x0000000000dc310c>, 3.0)
[6236]{L} >> cfunc: rb_obj_equal[@0x7f1d771b7d40](#<Symbol:0x0000000000dc310c>, 3.0) -> false
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, :foo) -> false
[6236]{L} >> branchif +53 (0x56106f3ec898->0x56106f3eca50) { false }; jump: not taken
[6236]{L} >> putobject (true)
[6236]{L} >> topn n: 1 [ true, :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: (true).=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (true).=== // cfunc.func: rb_equal[@0x7f1d771b7e50](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](true, :foo)
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](true, :foo) -> false
[6236]{L} >> branchif +49 (0x56106f3ec8d8->0x56106f3eca70) { false }; jump: not taken
[6236]{L} >> putnil
[6236]{L} >> topn n: 1 [ nil, :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: (nil).=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (nil).=== // cfunc.func: rb_equal[@0x7f1d771b7e50](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](nil, :foo)
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](nil, :foo) -> false
[6236]{L} >> branchif +46 (0x56106f3ec910->0x56106f3eca90) { false }; jump: not taken
[6236]{L} >> putobject ("foo")
[6236]{L} >> topn n: 1 [ "foo", :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: ("foo").=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("foo").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", :foo)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", :foo) -> false
[6236]{L} >> branchif +42 (0x56106f3ec950->0x56106f3ecab0) { false }; jump: not taken
[6236]{L} >> putobject (:foo)
[6236]{L} >> topn n: 1 [ :foo, :foo ] (top->bottom)
[6236]{L} >> topn -> :foo
[6236]{L} >> opt_send_without_block: (:foo).=== :foo [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> putobject (true)
[6236]{L} >> leave -> true
[6236]{L} >> branchif +38 (0x56106f3ec990->0x56106f3ecad0) { true }; jump: taken
[6236]{L} >> pop (:foo)
[6236]{L} >> putstring "symbol"
[6236]{L} >> leave -> "symbol"
[6236]{L} >> putself (main)
[6236]{L} >> putstring "foo"
[6236]{L} >> opt_send_without_block: (main).foo "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> "foo"
[6236]{L} >> dup ("foo")
[6236]{L} >> dup -> TOPN(0): "foo"
[6236]{L} >>     -> TOPN(1): "foo"
[6236]{L} >> opt_case_dispatch key: "foo" (type:5), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through (:=== redefined)
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", "foo" ] (top->bottom)
[6236]{L} >> topn -> "foo"
[6236]{L} >> opt_send_without_block: ("hello").=== "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", "foo")
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", "foo") -> false
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { false }; jump: not taken
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> topn n: 1 [ 1, "foo" ] (top->bottom)
[6236]{L} >> topn -> "foo"
[6236]{L} >> opt_send_without_block: (1).=== "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (1).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, "foo")
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 69882184349060)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 1, 69882184349060) -> 69882184349060
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", 1)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", 1) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, "foo") -> false
[6236]{L} >> branchif +61 (0x56106f3ec818->0x56106f3eca10) { false }; jump: not taken
[6236]{L} >> putobject (2147483648)
[6236]{L} >> topn n: 1 [ 2147483648, "foo" ] (top->bottom)
[6236]{L} >> topn -> "foo"
[6236]{L} >> opt_send_without_block: (2147483648).=== "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (2147483648).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, "foo")
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 69882184349060)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 2147483648, 69882184349060) -> 69882184349060
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", 2147483648)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", 2147483648) -> false
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, "foo") -> false
[6236]{L} >> branchif +57 (0x56106f3ec858->0x56106f3eca30) { false }; jump: not taken
[6236]{L} >> putobject (3.0)
[6236]{L} >> topn n: 1 [ 3.0, "foo" ] (top->bottom)
[6236]{L} >> topn -> "foo"
[6236]{L} >> opt_send_without_block: (3.0).=== "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (3.0).=== // cfunc.func: rb_float_equal[@0x7f1d771acff0](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, "foo")
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{{:===>{}, :join=>{}, :foo=>{}, :====>{...}, :inspect=>{{...}=>true, {...}=>true, {...}=>true}}=>true}, :inspect=>{}}, :===) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 69882184349060)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, 3.0, 69882184349060) -> 69882184349060
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", 3.0)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", 3.0) -> false
[6236]{L} >> cfunc: rb_float_equal[@0x7f1d771acff0](#<Float:0x0040000000000002>, "foo") -> false
[6236]{L} >> branchif +53 (0x56106f3ec898->0x56106f3eca50) { false }; jump: not taken
[6236]{L} >> putobject (true)
[6236]{L} >> topn n: 1 [ true, "foo" ] (top->bottom)
[6236]{L} >> topn -> "foo"
[6236]{L} >> opt_send_without_block: (true).=== "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (true).=== // cfunc.func: rb_equal[@0x7f1d771b7e50](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](true, "foo")
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](true, "foo") -> false
[6236]{L} >> branchif +49 (0x56106f3ec8d8->0x56106f3eca70) { false }; jump: not taken
[6236]{L} >> putnil
[6236]{L} >> topn n: 1 [ nil, "foo" ] (top->bottom)
[6236]{L} >> topn -> "foo"
[6236]{L} >> opt_send_without_block: (nil).=== "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (nil).=== // cfunc.func: rb_equal[@0x7f1d771b7e50](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](nil, "foo")
[6236]{L} >> cfunc: rb_equal[@0x7f1d771b7e50](nil, "foo") -> false
[6236]{L} >> branchif +46 (0x56106f3ec910->0x56106f3eca90) { false }; jump: not taken
[6236]{L} >> putobject ("foo")
[6236]{L} >> topn n: 1 [ "foo", "foo" ] (top->bottom)
[6236]{L} >> topn -> "foo"
[6236]{L} >> opt_send_without_block: ("foo").=== "foo" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("foo").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", "foo")
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("foo", "foo") -> true
[6236]{L} >> branchif +42 (0x56106f3ec950->0x56106f3ecab0) { true }; jump: taken
[6236]{L} >> pop ("foo")
[6236]{L} >> putstring "string"
[6236]{L} >> leave -> "string"
[6236]{L} >> putself (main)
[6236]{L} >> putobject (2147483648)
[6236]{L} >> opt_send_without_block: (main).foo 2147483648 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> 2147483648
[6236]{L} >> dup (2147483648)
[6236]{L} >> dup -> TOPN(0): 2147483648
[6236]{L} >>     -> TOPN(1): 2147483648
[6236]{L} >> opt_case_dispatch key: 2147483648 (type:-1), hash: {"hello"=>72, 1=>76, 2147483648=>80, 3=>84, true=>88, nil=>92, "foo"=>96, :foo=>100}; path taken: fall through (:=== redefined)
[6236]{L} >> putobject ("hello")
[6236]{L} >> topn n: 1 [ "hello", 2147483648 ] (top->bottom)
[6236]{L} >> topn -> 2147483648
[6236]{L} >> opt_send_without_block: ("hello").=== 2147483648 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: ("hello").=== // cfunc.func: rb_str_equal[@0x7f1d77277400](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 2147483648)
[6236]{L} >> cfunc: rb_str_equal[@0x7f1d77277400]("hello", 2147483648) -> false
[6236]{L} >> branchif +64 (0x56106f3ec7e0->0x56106f3ec9f0) { false }; jump: not taken
[6236]{L} >> putobject_INT2FIX_1_ [putobject (1)]
[6236]{L} >> topn n: 1 [ 1, 2147483648 ] (top->bottom)
[6236]{L} >> topn -> 2147483648
[6236]{L} >> opt_send_without_block: (1).=== 2147483648 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (1).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 2147483648)
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](1, 2147483648) -> false
[6236]{L} >> branchif +61 (0x56106f3ec818->0x56106f3eca10) { false }; jump: not taken
[6236]{L} >> putobject (2147483648)
[6236]{L} >> topn n: 1 [ 2147483648, 2147483648 ] (top->bottom)
[6236]{L} >> topn -> 2147483648
[6236]{L} >> opt_send_without_block: (2147483648).=== 2147483648 [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_sendish [inlined]: inline method cache hit, cd->cc->call_: vm_call_cfunc_with_frame[@0x7f1d772c35e0]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6ed0, ec->cfp: 0x7f1d6dbe6ed0
[6236]{L} >> vm_call_cfunc: (2147483648).=== // cfunc.func: rb_int_equal[@0x7f1d771ae510](argc: 1); cfunc.invoker: ractor_safe_call_cfunc_1[@0x7f1d772bba60]
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 2147483648)
[6236]{L} >> cfunc: rb_int_equal[@0x7f1d771ae510](2147483648, 2147483648) -> true
[6236]{L} >> branchif +57 (0x56106f3ec858->0x56106f3eca30) { true }; jump: taken
[6236]{L} >> pop (2147483648)
[6236]{L} >> putstring "bignum"
[6236]{L} >> leave -> "bignum"
[6236]{L} >> newarray num: 7 [ "world", "num", "float", "float", "symbol", "string", "bignum" ]
[6236]{L} >> newarray -> ["world", "num", "float", "float", "symbol", "string", "bignum"]
[6236]{L} >> setlocal_WC_0 [email protected]:1(raw:3)[2/2], level:0, val: (["world", "num", "float", "float", "symbol", "string", "bignum"])
[6236]{L} >> putself (main)
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:4)[1/2], level:0
[6236]{L} >> getlocal_WC_0 -> ["world", "num", "float", "float", "symbol", "string", "bignum"]
[6236]{L} >> getlocal_WC_0 [email protected]:1(raw:3)[2/2], level:0
[6236]{L} >> getlocal_WC_0 -> ["world", "num", "float", "float", "symbol", "string", "bignum"]
[6236]{L} >> newarray num: 2 [ ["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"] ]
[6236]{L} >> newarray -> [["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]]
[6236]{L} >> opt_send_without_block: ([["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]]).inspect [flags:ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6f10, ec->cfp: 0x7f1d6dbe6f10
[6236]{L} >> vm_call_cfunc: ([["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]]).inspect // cfunc.func: rb_ary_inspect[@0x7f1d77075ec0](argc: 0); cfunc.invoker: ractor_safe_call_cfunc_0[@0x7f1d772bba50]
[6236]{L} >> cfunc: rb_ary_inspect[@0x7f1d77075ec0]([["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]])
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{}, :inspect=>{{...}=>true, {...}=>true}}, :inspect)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{}, :inspect=>{{...}=>true, {...}=>true}}, :inspect) -> {}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, [["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]], true)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({}, [["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]], true) -> true
[6236]{L} >> cfunc: rb_ary_inspect[@0x7f1d77075ec0](["world", "num", "float", "float", "symbol", "string", "bignum"])
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{}, :inspect=>{[...]=>true, {...}=>true, {...}=>true}}, :inspect)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{}, :inspect=>{[...]=>true, {...}=>true, {...}=>true}}, :inspect) -> {[...]=>true, {...}=>true}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({[...]=>true, {...}=>true}, ["world", "num", "float", "float", "symbol", "string", "bignum"], true)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({[...]=>true, {...}=>true}, ["world", "num", "float", "float", "symbol", "string", "bignum"], true) -> true
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("world")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("world") -> "\"world\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("num")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("num") -> "\"num\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float") -> "\"float\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float") -> "\"float\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("symbol")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("symbol") -> "\"symbol\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("string")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("string") -> "\"string\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("bignum")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("bignum") -> "\"bignum\""
[6236]{L} >> cfunc: rb_ary_inspect[@0x7f1d77075ec0](["world", "num", "float", "float", "symbol", "string", "bignum"]) -> "[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]"
[6236]{L} >> cfunc: rb_ary_inspect[@0x7f1d77075ec0](["world", "num", "float", "float", "symbol", "string", "bignum"])
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{}, :inspect=>{[...]=>true, {...}=>true, {...}=>true}}, :inspect)
[6236]{L} >> cfunc: rb_hash_aref[@0x7f1d7713e270]({:===>{}, :join=>{}, :foo=>{}, :====>{}, :inspect=>{[...]=>true, {...}=>true, {...}=>true}}, :inspect) -> {[...]=>true, {...}=>true}
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({[...]=>true, {...}=>true}, ["world", "num", "float", "float", "symbol", "string", "bignum"], true)
[6236]{L} >> cfunc: rb_hash_aset[@0x7f1d7713a880]({[...]=>true, {...}=>true}, ["world", "num", "float", "float", "symbol", "string", "bignum"], true) -> true
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("world")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("world") -> "\"world\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("num")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("num") -> "\"num\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float") -> "\"float\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("float") -> "\"float\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("symbol")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("symbol") -> "\"symbol\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("string")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("string") -> "\"string\""
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("bignum")
[6236]{L} >> cfunc: rb_str_inspect[@0x7f1d77279b70]("bignum") -> "\"bignum\""
[6236]{L} >> cfunc: rb_ary_inspect[@0x7f1d77075ec0](["world", "num", "float", "float", "symbol", "string", "bignum"]) -> "[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]"
[6236]{L} >> cfunc: rb_ary_inspect[@0x7f1d77075ec0]([["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]]) -> "[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]"
[6236]{L} >> opt_send_without_block: (main).puts "[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]" [flags:FCALL|ARGS_SIMPLE]
[6236]{L} >> vm_call_cfunc(_with_frame): reg_cfp: 0x7f1d6dbe6f10, ec->cfp: 0x7f1d6dbe6f10
[6236]{L} >> vm_call_cfunc: (main).puts // cfunc.func: rb_f_puts[@0x7f1d77155730](argc: 1, defined argc: -1); cfunc.invoker: ractor_safe_call_cfunc_m1[@0x7f1d772bba40]
[6236]{L} >> cfunc: rb_f_puts[@0x7f1d77155730](0x1, ["[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]"], main)
[6236]{L} >> cfunc: rb_io_puts[@0x7f1d771554d0](0x1, ["[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]"], #<IO:<STDOUT>>)
[6236]{L} >> cfunc: io_write_m[@0x7f1d77151e00](0x2, ["[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]", "\n"], #<IO:<STDOUT>>)
[6236]{L} >> cfunc: io_write_m[@0x7f1d77151e00](0x2, ["[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]", "\n"], #<IO:<STDOUT>>) -> 133
[6236]{L} >> cfunc: rb_io_puts[@0x7f1d771554d0](0x1, ["[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]"], #<IO:<STDOUT>>) -> nil
[6236]{L} >> cfunc: rb_f_puts[@0x7f1d77155730](0x1, ["[[\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"], [\"world\", \"num\", \"float\", \"float\", \"symbol\", \"string\", \"bignum\"]]"], main) -> nil
[6236]{L} >> leave -> nil
[6236]{L} >> getlocal_WC_0 [email protected]:0(raw:3)[1/1], level:0
[6236]{L} >> getlocal_WC_0 -> #<TracePoint:enabled>
[6236]{L} >> opt_send_without_block: (#<TracePoint:enabled>).disable [flags:ARGS_SIMPLE]
[6236]{L} >> putnil
[6236]{L} >> leave -> nil
[6236]{L} >> opt_invokebuiltin_delegate "tracepoint_disable_m" (tracepoint_disable_m[@0x7f1d772ea2e0]), index: 0x0
[6236]{1} [["world", "num", "float", "float", "symbol", "string", "bignum"], ["world", "num", "float", "float", "symbol", "string", "bignum"]]
[6236]{1}
[6236]{2 -> EOF}
[6236]{1 -> EOF}
[*] onChildDetached(reason='process-terminated')

New Features

In addition to support for Ruby 3.1, this new release of ruby-trace also includes a number of other improvements.

Test Enhancements

While it was already the case that ruby-trace had a better and more complete set of test cases for emitting opcodes and covering edge cases in the Ruby VM than Ruby itself, this release includes an overhauled test infrastructure to better ensure correctness and test coverage. This release also includes several fixes to edge cases caught by the new test harness, including modifications to test cases that ensure emission of specific opcodes across applicable Ruby versions as the Ruby bytecode compiler will often change what sorts of opcodes get emitted for certain code constructs.

Custom Trace Enable

ruby-trace uses the TracePoint API infrastructure to ensure that tracing can reliably be enabled and, more importantly, disabled, regardless of if the code being traced raises exceptions or performs other weird control flow changes that might otherwise evade a disable tracing call. The current version of ruby-trace now exposes a custom Ruby-facing interface to enable and disable tracing without hitching a ride on the Ruby tracing API, and a separate CLI feature to enable tracing whenever a specific native function is executed. These make it possible to perform more fine-grained tracing without also running Ruby’s own tracing at the same time.

Future Work

Ractors

ruby-trace doesn’t yet fully support the use of multiple Ruby execution contexts, and therefore cannot easily distinguish between ractors in Ruby 3.0+. Part of this is due to caching of rb_execution_context_t values and part of this is due to needing to carefully track the creation and use of them in the first place, with the limited number of non-elided symbols available. However, adding support for ractors is on the todo list, so look forward to it.

Early Bringup

In very early versions of ruby-trace before its tracing on-off switches were anchored to the TracePoint API, it would instrument from the earliest moments of the start of the Ruby VM in rb_call_inits, including the construction of the BasicObject class, which happens fairly late in the grand scheme of things. While very cool, this is also a very difficult context to work within as even the infrastructure for symbols and IDs don’t really exist until just before the logic to create the class hierarchy and Object and BasicObject, let alone any operations that require the VM to be functional to execute opcodes. As a result, stringification for trace output had to be very simple so as not to crash the process due to lower level support functions not being runnable so early in VM bringup. So ruby-trace was modified to use Init_Object as its main anchor to safely apply its other hooks, including the hooks to enable the tracing hooks when the TracePoint API was invoked.

While it “should”™ be a simple enough affair to enable a CLI flag to simply begin tracing after rb_call_inits returns, it would be nice to try to begin tracing from the point that Init_Object returns, and ideally, from the start of Init_BareVM or even ruby_setup/ruby_init. However, this is likely going to require some work, and may require parallel hooking infrastructure(s) tailored to the lack of a runtime substrate in the different phases of Ruby bringup.

Trace Control

Right now, any library code that interacts with Ruby’s own tracing APIs could very easily accidentally disable ruby-trace’s tracing. Going forward, I’ll likely add some features to better control what can and cannot enable and disable its tracing. Additionally, it would be useful to provide CLI-based class/module method filters to enable tracing either until the method returns, or simply until disabled, obviating the need to write any Ruby to trace components of existing Ruby applications. I would also like to make the hooks configurable to, for example, trace only certain opcodes, but not native calls, etc., or to disable tracing entirely for certain methods as large quantities of trace output can be daunting to work with.

Conclusion

ruby-trace has been a fun hobby project to work on, if only for the work in reversing Ruby and triaging some truly deranged bugs. I’m not sure if it could ever be considered “stable,” but it mostly exists to prove a point. Overall, I think the Ruby developers should just add a proper tracing implementation, even if it’s not terribly performant. It would certainly make my life easier.

Tool Release – shouganaiyo-loader: A Tool to Force JVM Attaches

29 December 2021 at 22:38

Background

Java Virtual Machines (JVMs) provide a number of mechanisms to inspect and modify the Java applications and the runtime they stand on. These include Java agents, JARs that are capable of modifying Java class files at runtime; and JVMTI agents, native libraries that can perform deep hooking into the innards of the JVM itself. In fact, the former are built on top of the latter, which also powers the JDWP (Java Debug Wire Protocol) server implementation that nearly all Java “debuggers” are clients of. While the Java (JAR) agents are often configured in arguments of the Java command used to start an application, both Java and JVMTI (JVM Tools Interface) agents are often connected at runtime to analyze performance issues or other facets of applications running on the JVM.

These capabilities are also useful when attempting to reverse engineer or modify running Java processes. Due to this, software developers will often introduce anti-debugging into their applications that attempt to detect the use of or disable such features. While many are trivially disabled through the use of the capabilities they try to detect, some can be troublesome when it is not feasible to modify the initial command line arguments of the process. In such situations, options that disable the underlying agent attach API can be annoying to get around as this prevents such agents from running in the first place. While these options are legitimately used as a means to rein in the insecure default model that enables any process run by the same user to inject arbitrary code into a running JVM process, attempts to use them for anti-debugging are misguided at best.

To get around such futile practices, I wrote shouganaiyo-loader, a Frida-based Java/JVMTI agent loading tool that forces JVMs to load agents. This tool enables simple injection of Java agents into JVM processes, regardless of their having disabled dynamic agent loading, making it easy to hot instrument a Java app that can’t go down, say for example… to stop active exploitation of a vulnerability in a major logging framework.

shouganaiyo-loader

shouganaiyo-loader is a cross-platform Frida-based command-line tool that forces Java processes to load a Java/JVMTI agent regardless of whether or not the JVM has disabled the agent attach API. It currently supports the HotSpot and OpenJ9 JVMs on Linux, macosx, and Windows and uses JVM- and platform-specific techniques to re-enable runtime agent attachment.

In Sun/Oracle HotSpot, the attach API is implemented entirely in platform-specific native code; shouganaiyo-loader performs the necessary memory patching, function hooking, and code injection to load agents. An interesting facet of HotSpot discovered while writing this tool is that, on Windows, the -XX:+DisableAttachMechanism option is unenforced. HotSpot’s Windows attachment protocol is a direct OpenProcess call combined with VirtualAllocEx, WriteProcessMemory, VirtualAllocEx, and CreateRemoteThread calls that inject and run a C function into the JVM process that invokes a Windows-specific JVM_EnqueueOperation dynamically sourced from the PE export table via GetModuleHandle and GetProcAddress. The only reason the normal clients fail to work on Windows is that this option also disables creation of the metadata files that the clients use to identify attachable JVM processes.

In IBM/Eclipse OpenJ9, nearly the entire agent API implementation is written in cross-platform Java code. While invoking this code via Frida does not require the level of native struct spoofing as used to invoke the internal HotSpot APIs, it does present its own challenges. However, it is simple enough, if tedious, to use Frida to wrap the JNI APIs that enable C/C++ code to perform reflection and invoke the necessary Java methods. This code was written before Frida released support for the JVM, which appears to be JVMTI-based, at least in part, and it seems safer to rely on just the JNI APIs to avoid any potential adverse conflicts with the Java or JVMTI agents we are attempting to load.

Usage

shouganaiyo-loader is a Node.js command-line tool. It can be used to trivially inject a Java agent JAR (or JVMTI library) into a target process as follows:

# shouganaiyo-loader -p <pid> -t <hotspot|openj9> -a /path/to/myagent.jar 'java agent arguments'
# shouganaiyo-loader -p <pid> -t <hotspot|openj9> -j /path/to/myagent.so  'jvmti agent arguments'

It can be installed from NPM with npm install -g shouganaiyo-loader.

Technical Advisory – Lenovo ImController Local Privilege Escalation (CVE-2021-3922, CVE-2021-3969)

15 December 2021 at 22:29
Vendor: Lenovo
Vendor URL: https://www.lenovo.com/
Versions affected: 1.1.20.2
Systems Affected: Windows
Author: [email protected]
Advisory URL: https://support.lenovo.com/us/en/product_security/LEN-75210
CVE Identifier: CVE-2021-3922, CVE-2021-3969
Risk: AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H/E:P/RL:U/RC:R CVSSv3.1: 7.1

Summary

The ImController service comes installed on certain Lenovo devices, for example NCC found the service installed on a ThinkPad workstation. The service runs as the SYSTEM user and periodically executes child processes which perform system configuration and maintenance tasks.

Impact

Elevation of privilege. An attacker can elevate their privileges to that of the SYSTEM user from a user that is able to write files to the filesystem.

Details

A vulnerability exists in the way in which the ImControllerService handles the execution of highly privileged child processes which allows an unprivileged attacker with local access to the system to elevate their privileges.

The vulnerable component periodically starts child processes to perform tasks. Each child process immediately opens a named pipe server to which any user on the system can connect. The parent process establishes a connection to the child’s server as soon as possible in order to send XML serialised commands over the named pipe. The child does not validate the source of the connection and parses the XML serialized commands. One of the commands that the parent process can send instructs the child to load a ‘plugin’ from an arbitrary location on the filesystem. The child process validates the digital signature of the plugin DLL file before loading the file into its address space and yielding execution to it.

Successful exploitation of two vulnerabilities required to get the child to load a payload of the attacker’s choosing.

The first vulnerability is a race condition between an attacker and the parent process connecting to the child process’ named pipe. An attacker using high performance filesystem synchronization routines can reliably win the race with the parent process to connect to the named pipe. During testing NCC Group’s proof of concept code never failed to connect to the named pipe before the parent service could do so. Because the child process does not validate the source of the connection it will begin accepting commands from the attacker after the race condition has been exploited.

The following picture displays an instance of the named pipe server in the child process. The pipe is accessible for the EVERYONE group on the system for write access and is always named ‘pipe_’ followed by a random GUID.

The second vulnerability is a time-of-check to time-of-use (TOCTOU) vulnerability. When the child process is sent the command to load a plugin it first validates if the plugin is signed by Lenovo. The vulnerability exists because the child process releases all open handles to the file once the validation has succeeded and before the file is loaded. In order to increase the reliability of this attack, the attacker can use opportunistic locking (OpLocks) on a file that is accessed by the child process between the validation and the load operation. This file access is a by-product of the ImControllerService plugin loading process and is also fully under the attacker’s control. Once the OpLock is placed, the attacker can stall the loading process and replace the now validated plugin with a DLL file of the attacker’s choosing. Once the lock is released the child process loads the DLL, leading to escalation of privilege.

The following screenshot displays the relevant events in the exploitation chain. The events in the red box show the final steps in the verification process and all handles to the DLL being released making the plugin file writable again for the attacker. The events in the purple box show the dependency file being accessed that the attacker can use to stall the load operation using OpLocks. The final box displays events related to the load operation.

The screenshot below shows NCC Group’s proof of concept exploit working against a test installation of the ImControllerService, the steps taken during the exploit and dialog presented by the attacker controlled DLL launched loaded by the child process.

Recommendation

Update the IMController component of Lenovo System Interface Foundation to version 1.1.20.3.

Vendor Communication

Issue reported to Lenovo PSIRT: 29/10/2021 
Lenovo confirms the issue: 02/11/2021
Lenovo releases patch: 17/11/2021
Lenovo releases advisory: 14/12/2021
NCC Group releases advisory: 15/12/2021

Thanks to

Aaron Haymore, [email protected]

The professional, cordial and responsive security team of Lenovo, who received this report and were able to reproduce, and fix, the issue very quickly.

About NCC Group

NCC Group is a global expert in cybersecurity and risk mitigation, working with businesses to protect their brand, value and reputation against the ever-evolving threat landscape. With our knowledge, experience and global footprint, we are best placed to help businesses identify, assess, mitigate & respond to the risks they face. We are passionate about making the Internet safer and revolutionizing the way in which organizations think about cybersecurity.

Published date:  15-12-2021

Written by:  Rick Veldhoven

Choosing the Right MCU for Your Embedded Device — Desired Security Features of Microcontrollers

15 December 2021 at 15:14

The Microcontroller Unit (MCU) is the heart of an embedded device, where the main firmware executes its instructions to carry out the system’s functions. These come in many varieties. Relatively simple microcontrollers with limited-resource processors may bundle only a few IO peripherals, a small amount of memory, and be intended to run a small real-time operating system (RTOS) or bare metal firmware. More complex System-on-Chip (SoC) devices may contain a dozen or more CPUs (some dedicated to specific functions), a wide assortment of peripheral controllers, and enough horsepower to run Android or a full-blown desktop operating system. Still others may be more exotic, use digital signal processors (DSP), leverage reconfigurable logic with the CPU implemented as a soft-core, or use application-specific architectures dedicated to specific workloads like a neural processor or a GPU. To simplify the discussion, we will use the imprecise term “microcontroller” to refer to all of these. Additionally, microcontrollers are rarely sold in isolation, and most often include an entire Board Support Package (BSP) of development tools and firmware intended by their vendors to help get your product to market as fast as possible.

Understanding the security offered by the BSP and MCU before making a selection can make an enormous difference to the overall security posture of the final product and save expensive design iterations. Additionally, at the time of this writing, due to the lingering effects of the COVID-19 pandemic, trade disputes, and other factors, there is a semiconductor shortage with up to 24-month lead times for some parts. This will force some device manufacturers to make hard decisions and rethink which chipsets power their products and they may instead need to limit choices to those available. This is a compromise, but a compromise that needs to be well understood. Ideally, a proper alternative MCU can be found that provides all the security features required by the product. During supply shortages prices inevitably rise. The price increases are anticipated to incentivize component counterfeiting and recycling. Parts must therefore be purchased from a reputable vendor to avoid spending money on old recycled MCUs, which may be rebranded to appear as if new.

This article intends to illuminate important security criteria that must be evaluated when choosing the right component for an embedded systems project. It will help inform the questions engineers should ask chip vendors before deciding which is the best microcontroller for their new product.

Security Considerations

Some MCUs cost a fraction of a dollar and therefore can be used in very inexpensive (or even disposable) IoT products. These typically have minimal security features; at most a simple checksum of the firmware may be calculated at device boot to ensure firmware integrity. On the other end of the spectrum, MCUs such as the ones based on ARM Cortex-M33, are powerful microcontrollers with hardware cryptographic accelerators that are able to run embedded Linux and have support for ARM’s TrustZone technology. 

The security requirements of a product must be adjusted based on the environment in which they operate. A device deployed in an environment with additional security controls, such as behind locked doors, may have vastly reduced requirements compared to a portable device that is more likely to be lost or stolen. 

Additionally, the requirements must adjust based on the sensitivity of the data processed by the device. Two similarly powered microcontrollers might “do the job”, but the one supporting strong security will be a better option to protect sensitive assets such as private user data, a cryptocurrency wallet, or access to administrative functions affecting other users.

Desired Security Features

Last week, NCC Group published a blog showing the risks embedded devices face. We’d recommend the reader spend a few minutes reviewing it before diving into this section, as it explains the security concerns the OEM vendors and their customers are exposed to if device security is just an afterthought. Take your time, we’ll wait…

Okay… now armed with a good understanding of these risks, let us discuss what kind of security features and properties embedded devices should include to mitigate these risks. As the saying goes, “a chain is no stronger than its weakest link”. Attackers are opportunistic and will find the easiest way to compromise your device.

Not all of the following security features presented below are equal in value. Features such as flash encryption are designed to protect against physical attacks and therefore are very useful for applications such as access control where an attacker may have access to the badge used to enter a building. The same feature is still useful, but less valuable for a device such as a consumer-grade router that is often behind locked doors. More valuable are the features that mitigate against remote attacks or do not allow code modifications. Refer to your product threat model when evaluating the importance of each feature.

Firmware Verification

Here we refer to the microcontroller’s ability to validate software before it is executed. Devices supporting Secure Boot will perform a cryptographic validation of the firmware before executing it, thus ensuring that only code signed by the manufacturer (or other authorized party) is allowed to run. Each firmware component in turn validates the subsequent firmware component as it is loaded to ensure that the system only runs authenticated firmware. The foundation of trust must be rooted in hardware, which means the microcontroller must support a hardware Root of Trust.

Additionally, vendor firmware should validate firmware updates before writing them to flash. On devices with Secure Boot an invalid update may not be able to compromise the device, but it may brick it. On devices without Secure Boot, however, verifying the firmware updates is critical as it is the only way to ensure legitimate code is running on the device. This is the case for many microcontrollers running from internal flash or EEPROM. There have been many instances in which attackers were able to force the device to retrieve the firmware update from an attacker-controlled location. As such, intruders were able to write their own firmware that set the stage for a remote attack against the users’ local network.

Hardware Root of Trust

A microcontroller is said to have a Root of Trust (RoT) when it starts by running trusted firmware, such as the ROM code which is set at chip manufacturing time, immutable, and therefore implicitly trusted. In some cases, the use of internal flash within the microcontroller can mimic this behavior, provided suitable flash protections are in place. This flash-based bootloader is programmed by the OEM and then permanently write-protected.

One-time-programmable (OTP) fuses are generally used to permanently configure the Secure Boot functionality, by enabling it, and burning the public key (or certificate, or hash of it) that is used to validate the firmware. In this case the OTP fuses are part of the RoT.

In other words, a device has a Root of Trust if it starts by running code from an internal/immutable memory with an immutable configuration. It represents the foundation for the security layers built on top. If the authenticity of this early loaded software cannot be enforced, all the subsequent security layers can be modified or bypassed. 

At the inexpensive end of the spectrum microcontrollers tend not to have this feature, and allow any firmware to run. In this case, a bug in the code that allows writes to flash could persistently compromise the device and give attackers full control over the firmware.

Rollback Protection

This feature protects against attacks in which the attacker may update or replace the firmware with an older version in order to exploit known vulnerabilities in the older version. Since the older vulnerable firmware was legitimately signed by the vendor, the microcontroller will accept and execute the firmware. This defense is primarily of benefit when the user, owner, or administrator is considered a potential attacker. This is the case for example with jailbreaking of game consoles, or when no authorization is needed to perform firmware updates (which is the case for many Android devices).

An effective anti-rollback mechanism typically uses a monotonically increasing counter that represents the lowest version of the firmware that is accepted. This counter must be stored within the microcontroller’s secure storage (commonly OTP fuses or internal flash memory) that itself cannot be rolled back. The rollback check must be performed when the device boots up, but ideally should also be verified before a firmware update. An older version should be detected and disallowed. Some chip vendors offer this feature out of the box and make it relatively straightforward to enable and use by providing reference code.

Secure Storage and Data Protection

This section is more relevant for microcontrollers that run from an external flash. Here at NCC Group we routinely remove the flash memory from PCBs to extract credentials such as WiFi passwords and modify the firmware. Unfortunately, many product vendors do not take into consideration this type of attack. Some microcontrollers, such as the ESP32, make it easy to encrypt all external flash by provisioning a random 256-bit AES key at device manufacturing time. Others might encrypt only portions where user data is stored.

Choose a microcontroller that encrypts the flash to increase the difficulty of extracting user secrets or credentials. The encryption key should be unique to each device so that breaking one device will not compromise all similar devices. Recovering a key is usually a costly and time consuming process and that may only be acceptable to an attacker if a successful key recovery compromises thousands of devices.

Additionally, encrypting the code may increase the time required to find vulnerabilities, as attackers first need to defeat the encryption mechanism before they can start analyzing the code. However, it should only be considered an obfuscation mechanism that increases the time until vulnerabilities are discovered. It should not be by any means a substitute for Secure Boot or static code analysis and professional audits of the source code.

Hardware Entropy

If the embedded application requires access to a source of strong entropy, it is ideal to select for your product a microcontroller that has a hardware random number generator (HWRNG). The source of entropy is based on jitter coming from various sources such as thermal or audio noise or, more commonly, a ring oscillator. Note that some chip vendors certify their HWRNG implementations, a useful feature if your product also needs to be certified

Strong entropy must be unpredictable and uniformly distributed. In the past, due to the lack of a strong RNG, developers used the current date or time since boot as seed for pseudo-random number generators (PRNG) to generate secrets. While the output of the PRNG is uniformly distributed, it is not unpredictable. Since the PRNG implementation is not secret, there are limited seeds that an attacker can try in order to replicate the results of the PRNG. 

If the application requires entropy but it is too late to change the microcontroller in the product, at a minimum a strong random seed can be injected at manufacturing time from an external source. This seed needs to be securely stored to protect its confidentiality. This seed can later be used with a PRNG algorithm to generate random data that can be used for generating keys. The seed should be supplemented periodically with other sources such as battery voltage variations or user-based events, otherwise on each boot the random sequence will be repeated.

Choosing a microcontroller that generates poor entropy due to lack of hardware support can turn out to be costly. There are many examples of devices that were compromised due to generation of predictable keys.

Strong Cryptographic Algorithms

Some inexpensive processors do not have any support for cryptographic operations, or perhaps only support obsolete algorithms (MD5, SHA-1, DES/3DES, etc). Others have good support in the BSP, provided by the chip vendor. The more evolved microcontrollers can run cryptographic operations in hardware, with better performance and stronger secret protections. 

Several aspects need to be considered. If the application requires access to cryptographic functions, it is better to choose a component for which the vendor already provides an implementation, in software or hardware. Creating your own secret cryptographic algorithms or implementations is always a bad idea and chipset vendors often provide robust open-source cryptography libraries. This also allows the product team to focus on the functionality of the application rather than adding basic features they can get for free.

Cryptographic operations can be CPU-intensive therefore, depending on the frequency of use, choose a part that can handle the cryptographic load as well as the functionality of the application.

The firmware verification (that was discussed previously) normally involves hashing the firmware and verifying the provided signature using a public key with an asymmetric algorithm. If a weak crypto algorithm is used in this process, attackers may be able to bypass the verification and run compromised firmware.

Resistance to Fault Injection Attacks

Even if the device under attack implements Secure Boot, it may not be sufficient to guarantee the integrity of the device. In the past, researchers as well as hackers have performed many successful fault injection attacks. The most common types are voltage and clock glitching. While different in nature, they both have the same goal: get the chipset into a state where a few instructions fail to execute properly. By carefully timing the glitch, the attacker can selectively skip important instructions such as Secure Boot verifications or debug disablement. Realistically expect that most microcontrollers are not hardened against this type of attack. Only recently have vendors started to treat this problem seriously and implement countermeasures, as attacker techniques and tools have become increasingly available and low cost.

If the product has a requirement to defend against physical attacks, it is recommended to select a microcontroller that implements countermeasures, from a vendor with a reputable security track record. Inquire if there are, at a minimum, mitigations in software. Mitigations in both the ROM code and the bootloader are important.

Commitment to Firmware Patching

For a significant share of the inexpensive microcontrollers on the market it is safe to assume that they will never receive firmware updates. This is particularly dangerous for connected devices as, historically, many vulnerabilities have been discovered in communication libraries such as those supporting TLS.

Choose a microcontroller that has long-term support, from a company that has a proven record of maintaining the firmware with periodic patching. This includes vendor’s code and any third-party code that might be in use. A microcontroller running a long-term support version of a popular operating system will most likely receive the best support.

The only good firmware updates are the ones that are actually applied. After the device is launched, it is the responsibility of the product manufacturer to merge in available patches and build new versions of the firmware. This should be a periodic process in which the engineers review the latest changes or security bulletins. Making the installation process easy for users (or even automatic) is vital.

Security Track Record

Commitment to firmware patching is not the whole story. History tells us that some chip vendors have been better at writing secure firmware than others. Some have a long track record of providing quality code and public research reflects that. Others are at the other extreme and are plagued by a considerable number of CVEs.

Even when using an open-source project for the firmware, it is important to understand that the device security is rooted in the ROM code. If the ROM code has vulnerabilities, it can bypass all other verification mechanisms. It is important to understand if the chip vendor is committed to security by performing external security audits of the ROM code and employing strategies such as fuzzing and static analysis to increase the code quality.

Security has a cost, but it is a cost worth paying. Chip vendors providing periodic security bulletins for their products tend to be the ones most committed to security.

Memory Protection

The memory management unit (MMU) and the memory protection unit (MPU) are hardware features of microcontrollers that allow memory protection for specified memory areas. There exist microcontrollers that do not have any memory protections in place, but those are not going to be covered in this section and their use is discouraged. Security-wise it is a good idea to avoid microcontrollers without at least some form of memory protection as one vulnerability can translate in a full control of the device (attacker could write anywhere in memory, including memory mapped flash if supported). Of all microcontrollers providing memory protection, only a small percentage are using an MMU.

An MPU is used to assign access permissions to memory regions, which varies for each chipset. These regions can have subregions and could be configured to be as little as a few bytes long. Each of these regions could be restricted to only allow privileged access, set as read-only, or marked as non-executable.

While having an MPU gives a crude level of protection, a microcontroller with an MMU can do a lot more and is vital to support many software mitigations. The MMU uses a Page Table to store a mapping between physical and virtual addresses. Each entry in the table is known as a Page Table Entry. Like the MPU, these Page Tables contain permissions such as read, write, and execute and can be accessed only by the owner process (or a more privileged process like the kernel). Pages containing data can be marked writable but not executable, and pages containing executable code can be marked as executable but not writable to mitigate some buffer overflow attacks. Any violations will cause a page fault exception. Another benefit is process isolation by which an application trying to access a page reserved for another application will not succeed, again, causing a page fault. This might be particularly useful if there are less trusted third-party applications running on the device as well. Certain exploit mitigations such as Address Space Layout Randomization (ASLR) rely on the MMU. ASLR is a technique to randomize the locations of code and data structures so that attackers cannot rely on previously known memory locations. This helps mitigate some exploitation techniques such as return-to-libc and return-oriented-programming (ROP). The translation layer provided by the virtual to physical address mapping makes it straightforward to implement ASLR. 

Embedded OS

Chip vendors have a vested interest in adding support for their products to popular real-time operating systems such as Linux, mbedOS, FreeRTOS, and Zephyr. This way they can benefit from the multitude of software tools in the open-source ecosystem, without the additional cost of doing so in-house. This is also a great advantage for the OEM vendors, as the hardware layer is abstracted by the OS. This makes it easier to port the application to other (more powerful) microcontrollers without having to worry about the hardware-specific implementation. Additionally, some microcontrollers may reach the end of life and can no longer be purchased. Also, it may be easier to build a real-time application on an OS such as Embedded Linux.

Note however that not all operating systems are equal, and they have various levels of maturity. With support from chip vendors, some implement task isolation and have a separation of privileges, to minimize the effect of a single vulnerability. More basic firmware without an OS would only require one vulnerability for full system compromise. The ones that implement an OTA (over-the-air) update mechanism make it easy to deploy product updates. Secure storage may also be supported to complicate physical attacks. Some operating systems are open-source allowing a full analysis of the software stack and easier maintenance.

Programming Language and In-house Expertise

Bare metal firmware: While C is the most popular language used to develop bare metal firmware, there are some vendors who offer runtimes for higher-level language alternatives. MicroPython is one and it can be used on some Nuvoton microcontrollers as well as the ESP8266/ESP32 from Espressif. Embedded Lua is also seen in some cases.

Operating system: If the OEM BSP includes an OS such as Linux, it is possible to use many programming languages to develop the application: C, C++, Python, Java, Go, Rust, etc. Ideally, choose a memory safe programming language, such as Rust/Java/Go. This way entire classes of vulnerabilities can be largely avoided, such as buffer overflows. Note that even with languages like Rust some parts interfacing with the hardware need to be unsafe, as code needs to behave more like C. As such, the benefits of using a safe programming language do not apply to unsafe sections of code. Unsafe areas need to be carefully implemented, isolated, and rigorously tested.

It may be beneficial to consider your talent pool and choose a microcontroller supporting development in a safe programming language already used in the company. Since the developers are already experienced with the programming language, they will produce better quality code, faster.

Secure Debug and Authenticated Access

Debug interfaces such as UART and JTAG are of great help during product development. These can simplify firmware development and help find intermittent bugs, which are otherwise extremely hard to eliminate. But this advantage becomes a risk if these interfaces are unauthenticated and there is no way to disable them in production. As an example, devices without Secure Boot that perform over-the-air update validation may be protected against remote and local attacks but are easy targets to physical attacks. One common issue is that some vendors may protect a JTAG re-enable feature using a secret password shared across all devices. If that password leaks or is extracted from one device, it can be used to attack products from other vendors that are using the same chipset. 

Some microcontrollers disable debugging using one-time-programmable fuses. Once the development phase is done, production devices are configured to burn debug-disable fuses on first boot. On microcontrollers with internal flash storage, this is sometimes a setting that can be reset after a full wipe of the storage space is performed.

Support for Secure Manufacturing

While some OEM vendors still produce their devices in-house, it is increasingly more economical to outsource production to manufacturing partners to benefit from their economy of scale. In the Original Design Manufacturing (ODM) model, the manufacturing partner designs and produces the final product according to the OEM specifications. In the Contract Manufacturer (CM) model the manufacturing partner builds the device that was designed in detail by the OEM thus giving the OEM more control over the design and process (but at a higher cost than with an ODM model).

No matter if the product is built in-house or by a manufacturing partner it is important to be able to ensure the authenticity of the device after it leaves the factory floor. Without such safeguards, counterfeit devices may be allowed to authenticate with the cloud services that support the device. 

Typically, some per-device secret is provisioned to or is generated on each device on the production line. That secret, normally a cryptographic key, is later used to identify the device to the backend infrastructure through a challenge/response mechanism. The secret needs to be protected such that the attacker cannot steal or impersonate a device. It is best if it is saved in an internal storage or an area not readable by users. Even better, if the microcontroller supports it, saved in immutable e-fuses.

Regardless of the manufacturing model in use, the OEM should carefully choose or specify a microcontroller that can store the identity secret securely.

Conclusions

Creating a new product is not an easy task to begin with. However, it can be significantly simplified by starting with the right MCU/BSP combination. Choosing the right platform, provided by a vendor with a great history of security and patching can reduce the risk of a compromise. Also, a platform that provides a comprehensive software development kit will reduce the development time allowing the OEM to focus on developing their application, rather than porting or (even worse) writing code for common functionalities such as TLS. Additionally, choosing a platform that allows good debug can help improve the performance, quality, and stability of the product. Selecting a chipset to power the device solely on cost of hardware is bad news. A compromise, an increase in development time, and a poorly performing device that requires fixes at the end of the development process all translate to costs which, with proper planning, could have been avoided. Spending some time in the beginning understanding the benefits each MCU/BSP combination provides, and selecting one that best matches the product requirements is a worthwhile investment. 

Don’t Forget To Be Awesome!

I’d like to thank my colleagues Nick Galloway, Jeremy Boone and Rob Wood for their insightful suggestions and feedback provided while writing this blog.

FPGAs: Security Through Obscurity?

14 December 2021 at 09:00

Background


For the uninitiated, an FPGA is a field-programmable array of logic that is typically used to perform or accelerate some specific function (or functions) within a computer system. They are typically paired with a separate traditional microprocessor (or as part of a combined system-on-chip (SoC)) but can operate standalone as well.

They can be configured at startup, or at runtime (using a technique called ‘partial reconfiguration’). In terms of flexibility, they fall somewhere between ASICs (application-specific integrated circuits) which have a high engineering cost and cannot be modified once produced, and software running on a microprocessor which can be rapidly developed on the fly.

Our previous research publication on FPGAs noted:

The most common uses of FPGAs are areas within automotive, medical, factory equipment and defence equipment that required rapid concurrent processing.”

This statement remains true. However, since then the use of FPGAs has expanded. The increased availability of low-cost development boards, open-source hardware and tooling, as well as the ability to rent a high specification FPGA in the cloud has considerably lowered the entry costs.

In recent years, we have noticed a trend amongst our clients using FPGAs in areas such as the Internet of Things (IoT), accelerated cloud services, machine learning and platform firmware resilience. We’ve also noted that FPGAs help to faciliate the development of significant open-source hardware projects such as RISC-V and OpenTitan.

It is possible in the near future that we will see further migration to FPGAs as the microchip supply shortage begins to bite, with some advocating FPGAs as a logical solution to the issue of chip shortages, however the shortage of substrates will still pose a problem for FPGA manufacturers.

In this blog post we will consider the following themes:

Uses of FPGAs

First, let’s consider some of the more recent applications of FPGAs that we have observed.

Platform Firmware Resiliency

Both Intel and Lattice are using FPGAs to underpin their implementations of NIST’s Platform Firmware Resiliency Guidelines (SP800-193).

These guidelines provide a series of technical recommendations that go beyond the traditional definition of ‘root of trust’ to ensure that the lower-level components of a computer system (typically, the hardware and firmware components) can protect, detect and recover from attempts to make unauthorized changes to the systems.

Intel’s open-source implementation, targeted at their MAX10 device performs functions including validating board management controller (BMC) and platform controller hub (PCH) firmware, as well as monitoring the boot flows of these critical components to ensure they complete in a timely fashion. Lattice’s implementation isn’t open source but claims to have ‘demonstrated state machine-based algorithms that offer Nanosecond response time in detecting security breaches into the SPI memory.’

 …as a service (aaS)

Offering something ‘as a service’ has become de rigueur in the world of cloud computing, and FPGAs are no exception here.

Some vendors offer access to high-specification FPGAs as a pay-as-you-use service, opening a product that previously would have had very high upfront costs, to a much wider market. Others use FPGAs to provide powerful acceleration for specific applications, without requiring developers to develop and deploy their own bitstreams.

Cloud vendors understandably look to maximize the usage of their hardware, and multi-tenanted environments are commonplace in cloud computing. Although FPGA usage currently remains primarily single tenancy (at least in terms of the FPGA itself), these instances are often on cards that are multi-tenanted on a single F1 instance (allowing, for example AWS to provide 1, 2 or 8 FPGAs per instance). it seems inevitable that before long, multi-tenancy systems will become common.

This will introduce new potential threats including remote voltage fault injection and side-channel attacks. As both vendors, and consumers of this type of technology – it will be important to understand the threat model involved when sharing low-level access to hardware to minimize these type of attacks.

Financial Technology

Gathering momentum since the early 2000s, FPGAs today play an increasing role in financial technology applications – on both sides of the trade, as traders and brokers engage in a race to create the fastest algorithms and trading platforms.

High-frequency-trading (HFT) firms (hedge funds, or prop traders) have migrated their trading algorithms from highly optimized C applications, through to FPGAs, and in some cases are now investigating migrating these designs to ASICs. On the exchange or market side, brokers use technology to provide low-latency streams of market data, as well as running low-latency, and deterministic risk checking platforms.

A recent article suggested that FPGAs are pretty much the starting point now for HFTs. And the shift in technology has been so profound that, “In 2011, a fast trading system could react in 5 microseconds. By 2019, a trader told me of a system that could react in 42 nanoseconds”.

Previously requiring a great deal of investment, Xilinx have launched an open-source algorithmic trading reference design, with the goal of democratizing this type of technology – using a plug and play framework.

Computing-Intensive Scientific Research

Many fields of modern scientific research – including in areas such as high-energy physics and computational biology – need to process and filter vast amounts of data. FPGAs are increasingly being used to provide the necessary processing and filtering required to make these data loads manageable.

An example of this is the Large Hadron Collider (LHC) at CERN. In various experiments, FPGAs are being used to help look for significant events in data at a rate in the order of hundreds of terabytes per second, and are used as a coarse filter before passing data off to other systems.

In medicine, The Munich Leukimia Lab (MLL) noted that since switching to FPGA-based genome sequencing “what used to take us 20 hours of compute time can now be achieved in only 3 hours”.

Artificial Intelligence (AI) and Machine Learning (ML)

Machine learning and artificial intelligence is a rapidly evolving area. It is impacting all areas of technology and business, including our own. Two years ago we carried out a long-term research project into the potential of using machine learning for web application testing, Project Ava, and we continue to research adversarial techniques in this space.

GPUs are currently leading in this space, having the advantage of a wealth of development behind them (and indeed, are well suited in some ways to the requirements of machine learning algorithms) with popular libraries such as TensorFlow supporting them out of the box, and CUDA providing an easy way to rapidly develop models for GPUs.

While the biggest technology companies are jumping to their own bespoke silicon to improve their AI capabilities; increasingly, FPGAs are discussed as possible alternatives to GPUs in the broader market. There are a few downsides to GPUs. They increasingly run at high temperatures, and can struggle when moved out of a temperature-controlled datacentre environment into real-world (sometimes referred to as edge) use. Their often substantial power consumption also makes them less suitable for IoT and other ‘smart’ applications.

The other substantial challenge with FPGAs is the development overhead – which currently doesn’t exist in the same mature state as for GPUs, As we shall discuss later however, high level synthesis languages and tools are facilitating this ease and speed of development now for FPGAs too. Vendors are creating dedicated silicon and bespoke tooling to help facilitate this transition.

The Changing FPGA Technology Landscape

Having considered recent applications of FPGA technology, let explore the current technology landscape including how the vendor ecosystem is changing, the rise of open-source and hobbyist FPGA development and how tooling is abstracting away designers from the underlying logic on the FPGAs.

The Vendor Ecosystem is Expanding

At first glance, the vendor landscape remains dominated by two major players Intel (who now own Altera) and Xilinx (who are in the process of being acquired by AMD). However, increasingly significant roles are being played by the smaller vendors (in no particular order) Lattice, Microchip (who bought Atmel in 2016 and Microsemi in 2018), QuickLogic and Achronix.

Although after acquiring Altera, Intel initially seemed to push towards aligning their FPGAs offering much more closely with their Xeon processor range, there seems to have been little development of this product line since 2018. Instead, Intel and Xilinx both seem to be pushing towards building more powerful SoC devices, using ARM cores. At the leading edge, Intel are pushing ahead with their eASIC range creating a halfway house between FPGAs and ASIC development, while Xilinx have their Versal ACAP product that brings together elements of FPGA, GPU and CPU processing on a single die.

At the low power, low-cost end of the market, Lattice and Microchip seem to be really driving into the hobbyist / maker end of the market. Lattice have acknowledged the value of the moves toward open source tooling in helping this.

Open Source and ‘Hobbyist’ Development Boards Becoming Popular

There has been significant development in the open-source space. On the hardware side, businesses such as Patreon and Kickstarter are helping to facilitate a number of open-source hardware, and FPGA projects – again, lowering the barrier to entry to this type of technology.

On the toolchain side, Symbiflow looks like the leading contender to bring more people into the ecosystem. Their goal of creating ‘the GCC of FPGAs’, and providing an opensource toolchain compatible with multiple vendors is a world away from being tied to specific (and expensive) vendor tooling.

Even the vendors are beginning to release more code to the community: Xilinx currently have over two hundred GitHub repositories, and have open-sourced significant portions of their High Level Synthesis toolchain, Vitis – in an attempt to encourage greater adoption (more on this below).

High Level Synthesis (HLS) accelerates rapid prototyping, but potentially introduces security risk

This author believes that we are fast approaching if not already past the point where FPGAs are used regularly in a ‘drag and drop’ design flow.  This is being facilitated by significant investments in high level synthesis tooling, which allows developers to develop FPGA designs, without necessarily having to have a deep familiarity with the underlying register-transfer level (RTL).

Xilinx have made significant investments into their Vitis HLS toolchain, including, opening it up for open-source collaboration; and there are a number of other projects in development that allow the rapid prototyping and conversion to FPGA of algorithms developed in other languages, (e.g. Java, C++, OpenCL). Other projects are looking to automatically abstract machine learning algorithms such as TensorFlow, Pytorch etc to RTL.

It is also worth mentioning, an alternative approach – the development of new languages: one of which is the Chisel project that adds hardware primitives to the Scala programming language. While this technically isn’t a high level synthesis tool, in the sense of some of the projects mentioned above – the aims are similar: to provide a better way for designers to produce rich logic designs while abstracting away low-level details.

In some ways, this is analogous to the development of computer programming languages. In the early years, a limited number of low-level languages were used (e.g. Assembly, C, Pascal, ADA – remind anyone of VHDL?). Today, a plethora of high-level languages, libraries, and frameworks allow developers of all skillsets and backgrounds to create complex systems.

However, as with modern software development, it could lead to increased potential for security issues around data processing (for example, mass assignment – where extra data fields are passed around unnecessarily for developer convenience), issues around endianness and line feeds, or in some cases to vulnerabilities exploiting memory leaks around dynamic memory allocation.

It will be interesting to assess the quality of the output of some of these tools and highlight any potential issues these may create. Vulnerabilities in the output of popular tools are likely to be replicated across a wide range of products and applications.

For example, in a typical state-machine design, an RTL designer will carefully define the underlying registers and memories to store data, provide appropriate assignments to sanitize the data at reset, and on various changes of state.  They will also take great care to ensure that the system fails safe. The individual components will be carefully verified via testbenches, and in specific high-risk areas of code, formal verification may be used to mathematically validate the design.

It remains to be seen whether the myriad of tools and methods now becoming available, will be capable of generating RTL to these same rigorous standards. Hopefully it will lead to improvements similar to those seen from the migration away from memory-unsafe languages such as C, to memory-safe languages such as Python.

A Move Toward Open Source Hardware

Since 2016, there has been a proliferation of open-source intellectual property (IP). Whereas historically companies would jealously guard their IP – now there is a much wider recognition within the community that open source has huge value to offer the hardware or embedded community.

The best example of this is the RISC-V project, an open instruction set architecture (ISA) – designed to bring the benefits of open source and open standards to software and hardware architecture. In the security space, RISC-V has previously run a competition encouraging participants to design processors that can thwart malicious software-based attacks. RISC-V has also been used as a test-bed to prove out novel hardware security technologies like CHERI. This kind of openness could have profound implications for people’s trust and faith in the traditional closed processor IP model.

Lattice have been collaborating on RISC-V for a while, Microchip claim to have released the first RISC-V SoC FPGA development kit, while Intel have just (October 2021) released a version of their popular NIOS soft processor based on RISC-V.

Recent Vulnerabilities in FPGAs

Having set the scene, we turn to some well-publicized vulnerabilities related to FPGAs to get a better idea of where the state-of-the-security-art currently lies within this space.

Starbleed: A Full Break of the Bitstream Encryption of Xilinx 7-Series FPGAs

Before introducing the following vulnerability, it is important to define bitfile, or bitstream. This is, the image that gets loaded onto the FPGA to tell it what to do, and can be thought of as analogous with a compiled computer program.

Typically, FPGA vendors support bitstream encryption to allow OEMs (who enable this feature) to protect the contents of the bitstream which is typically considered to be sensitive intellectual property (IP).

The Starbleed (2020) vulnerability, detailed in The Unpatchable Silicon: A Full Break of the Bitstream Encryption of Xilinx 7-Series FPGAs, breaks both the authenticity and confidentiality of bitstream encryption. This allows an attacker to:

  • create their own bitfile masquerading as a legitimate bitstream
  • reverse engineer the legitimate bitstream, and potentially identify vulnerabilities

Xilinx acknowledged the issue in a design advisory in which they state:

The complexity of this attack is similar to well known, and proven, Differential Power Analysis (DPA) attacks against these devices and therefore do not weaken their security posture”.

This author’s opinion is that the attack presented in the paper is more straightforward to carry out from a technical perspective, and has a lower material cost overhead than DPA. It should therefore be taken seriously by designers, and the risks mitigated accordingly.

It’s also worth noting that although the barrier to entry for power analysis attacks against the Xilinx FPGAs in this instance probably remains relatively high from a material cost perspective; in the general case this is changing rapidly with tools like ChipWhisperer that are lowering the barrier to entry for such attacks. Indeed, an FPGA is available as a target example for training purposes.

Returning to the original Starbleed paper, the authors introduce:

novel low-cost attacks against the Xilinx 7-Series (and Virtex-6) bitstream encryption, resulting in the total loss of authenticity and confidentiality

There are some pre-requisites for the attack, namely:

  • Access to a configuration interface on the FPGA (either JTAG or SelectMap)
  • Access to the encrypted bitstream

Access to these two items may not be trivial, if security has been considered in the design of the product.

The JTAG or SelectMap interfaces may not be connected to an external processor, in which case physical access to the device may be required.

If they are connected to an external processor (or indeed, that processor is responsible for loading the encrypted bitstream onto the FPGA), then there should be multiple protections (including a carefully threat modelled minimised attack surface and application of principles of least privilege for any externally facing services) in place to reduce the risk of attack.

That said, both of these goals are very achievable to a well-resourced and motivated attacker with physical access to one or more devices.

Let’s now unpack the individual attacks themselves:

Attack 1: Breaking confidentiality

Researchers took advantage of various features of the FPGA but particularly: the malleability of Cipher Block Chaining (CBC) mode of AES being used by Xilinx, and a register (WBSTAR) in the FPGA that is not reset during a warm boot.

Put (very) simply: given the structure of the bitstream is a known quantity, it is possible to manipulate the bitstream to effectively trick the FPGA into placing one word of correctly decrypted data into the WBSTAR register. If this process is repeated multiple times, the entire contents of the bitstream are revealed. It is well worth reading the full paper to understand how this works in detail.

Even with the unencrypted bitstream recovered; reverse engineering a bitstream is not trivial. Mature tools such as IDA and Ghidra that make this substantially easier in software, currently projects such as HAL and Binary Abstraction Layer are in a much earlier stage of development.

Attack 2: Breaking authenticity

Given the above, the researchers were then able to use padding oracle attacks such as those described by Rizzo and Duong, to use the FPGA to encrypt a bitstream of their choice. It is possible to reproduce the HMAC tag at the end of the stream, as the HMAC key is part of the encrypted bitstream (already broken)!

There are various impacts of these vulnerabilities: as mentioned earlier, reverse engineering may lead to further vulnerabilities being identified in a specific product. Breaking authenticity raises the prospect of things like cloning of hardware devices, or bitstream tampering – for example, introducing malicious logic or a hardware trojan.

Thangrycat (AKA Cisco Secure Boot Hardware Tampering Vulnerability)

The Thangrycat vulnerability, was found in 2019 by researchers investigating the secure boot process used in Cisco routers.

It exploits flaws in the design of the Secure Boot process, in which an FPGA forms the Cisco Trust Anchor Module (TAm). The vulnerability leads to the complete breakdown of the Secure Boot process, which could lead to the ability to inject persistent malicious implants into both the FPGA and software.

Fundamentally, this attack stems from the Cisco Secure Boot process using a mutable FPGA as the root-of-trust, rather than a more traditional immutable trust anchor.

The attack is facilitated by a command injection vulnerability in the software that enabled remote, root shell access to the device – and a firmware update utility that does not perform any authentication of the supplied FPGA bitstream.

This indicates the importance of a defense-in-depth approach to security, as it is the chain of vulnerabilities, that ultimately leads to the persistent remote compromise of the device.

Xilinx ZU+ Encrypt Only Secure Boot Bypass

Another FPGA vulnerability discovered in 2019, impacts Xilinx’s latest Ultrascale devices and exploits issues in the “encrypt only” root of trust implementation – which allows secure boot without asymmetric authentication.

Ultimately, a failure to authenticate the boot header, allows an attacker to potentially tamper with the first-stage bootloader (FSBL) execution address – causing undetermined behaviour, or potentially bypassing Secure Boot with the execution of the attacker’s own controlled code.

Presumably this issue is – or will be – resolved in later silicon, but as yet Xilinx “continues to recommend the use of the Hardware Root of Trust (HWRoT) boot mode when possible“.

JackHammer: Efficient Rowhammer on Heterogeneous FPGA-CPU Platforms

Jackhammer (2019) isn’t technically a vulnerability of an FPGA – but rather a method of using an FPGA to ‘upgrade’ the well-documented Rowhammer vulnerability. In short, it uses the performance and direct path to memory that the FPGA has to abuse the DRAM more quickly.

This raises two interesting thinking points:

  • How adversaries might use FPGA to enhance or improve existing attacks. For example PCIleech, or creating more powerful bus interposers such as TPMGenie.
  • How little visibility the operating system has over the behavior of an FPGA attached to the system

Defending Against Threats to FPGA Security

The temptation (beyond some sort of glaring logic error or design flaw), with many potential attacks against FPGAs is to overlook or dismiss them as ‘too hard’ or ‘too complicated’ to be of interest to an attacker. Alternatively, it may be tempting to think that having enabled ‘security features’ on the FPGA, that’s the job done.

So, as someone currently using, or looking at using FPGAs – given the vulnerabilities detailed above – what threats should you consider when reviewing your design?

Firstly, and as typical in many threat modelling scenarios, we start with the path of least resistance. Deploying physical attacks against an FPGA potentially has many obstacles – and risk of getting caught. The most likely attacks are those that can be facilitated remotely, and with minimal effort.

Attack the Source

If you are a high-value target, consider your weakest link. For example if you are running a high-frequency trading platform, it is likely your FPGAs are being constantly revised and redeveloped regularly. How much care is being taken around the security of that codebase? How well trained are your developers in good security practices? Do you have mitigations protecting from malicious insiders?

Could a phishing attack on a developer, leading to compromise of a company’s IT systems, lead to some sort of discreet trojan being embedded in your design?

Attack the Software

In many cases, the FPGA might sit behind, or be protected by various software security mechanisms. Those need to work and be maintained. Patching, constant evaluation of attack surface, and regular testing are key.

If these are broken, this could open a new avenue of attack: replacing the bitfile. If appropriate care hasn’t been taken in implementing secure boot as in the cases shown above, loading a malicious bitfile into a system could be trivial. No physical access, or reverse engineering required.

Physical Attacks

High value deployments need to be sensitive to physical attacks. For example, an HSM, with an FPGA-based SoC at its core, may be an attractive target for theft, followed by offline physical attack. For this reason, HSMs often deploy physical tamper detection mechanisms to erase key material if attempts are made to compromise the device via physical means.

In consumer-grade hardware: consider an ‘evil maid’ scenario, that perhaps is using an FPGA to provide a root-of-trust or some other key security feature: but the device could temporarily be in the hands of an untrusted person (a courier, a shop assistant, a warehouse worker). What would the impact be if an attacker replaced the device, or the bitstream with one that they controlled? Or placed their own interposer between the FPGA and other components in the system?

Do the interfaces between these key components perform any validation or authentication on the data they are receiving, or just implicitly trust it?

In this context it is worth reflecting on the example of malware called GrayFish developed by the Equation Group. This malware was able to achieve persistence (beyond even reformatting of the computer hard drive) and is very difficult to detect and remove, due to the lack of firmware signing protection. Are there any opportunities where this same type of attack could present itself on your system?

In lieu of fixing the design, attacks like this can be somewhat mitigated using measures such as RF shielding and potting (enclosing the components and PCB in some sort of compound like epoxy) making successful dismantling of the product far more challenging and time consuming.

Relying on Security Via Obscurity

The Starbleed vulnerability described earlier in this post leads to the disclosure of a plain text bitstream, which, with sufficient time and effort invested, could result in the reverse engineering of the IP and anything previously considered secret within. While this is currently a high effort operation, tooling such as HAL,  Binary Abstraction Layer, and CHIPJUICE are making the reverse engineering of FPGA and ASIC netlists more accessible.

It is important to resist the temptation to see the FPGA as a complex opaque box that no-one would go to the effort of compromising, and instead recognise the decreasing level of effort that will be required to reverse engineer a complex bitstream in the not-so-distant future.

Consider your design again: but now, assume that bitstream confidentiality can be compromised. What is the worse-case scenario? Is it possible that an attack could identify hardcoded magic values that might activate development or debug functionality? Or even worse, hardcoded secrets that could facilitate privilege escalation?

Abuse of Functionality

We’ve seen earlier in this post that there is the possibility of abusing an FPGA to carry out a Rowhammer-style attack, with improved efficiency, and potentially doing so covertly.

If you are running an FPGA accelerator card on a system, what other data does that system have access to? Could the FPGA accelerator be abused or mishandled, to extract secrets from the host via DMA? Is this part of your threat model?

Are you on a cloud-based FPGA platform? Although at time of writing (November 2021), you likely don’t have to worry about remote side-channel attacks from others on the same FPGA, theseso-called ‘long wire’ attacks by others on the same FPGA could potentially be a scenario in future.

Implementation Errors

However your design is being generated, it is important to ensure adequate testing is in place to identify errors in the design.

For example, if your FPGA is processing data incoming from various sensors with different protocols (e.g. I2C, SPI), what happens if that data is invalid or goes out of the expected range? Can the design cope? Does it fail safe? These types of errors can be identified both by code review, and by fuzz testing.

Supply Chain Attacks

The potential for both software and hardware supply chain attacks appears to be gaining increased visibility. In Summer 2020, the UK government, as part of a drive to future-proof the digital economy, launched a call for views on cyber security in supply chains and managed service providers. In July this year, the European Union’s agency for Cybersecurity (ENISA) published a Threat Landscape for Supply Chain Attacks; and  in the United States as part of the Biden administration’s executive order aimed to improve the country’s cyber resilience. Section 4 pertains specifically to “Enhancing Software Supply Chain Security”, and directed NIST to identify guidelines for improving security in this area.

Recent examples of in this space include:

  • The software supply chain compromise of the SolarWinds Orion network monitoring software, in which a security product used by US government agencies, and many other big business was compromised – and legitimate updates supplied by SolarWinds were implanted with a software trojan which attackers could then use to access victim’s systems.
  • Vulnerabilities identified in the Ledger Nano X mean that is possible for an attacker who has access to the device post-manufacture but prior to reaching the end-user to potentially install malicious firmware onto the devices, which could then infect or manipulate the end-user’s computer systems.
  • Recent research by F-Secure on ‘Fake Cisco’ devices – whereby, genuine Cisco firmware, was found to run on counterfeit hardware. The clones in this instance, did not appear to contain any malicious backdoors – but to the average end-user who is either trying to source equipment in a rush, or perhaps cut-costs, these counterfeit devices may pass as genuine.  

It is also worth noting that the controversial and still unproven, Bloomberg investigation into a possible supply chain attack on SuperMicro customers has (if nothing else), significantly raised the profile of this type of attack.

This recent Microsoft blog and presentation by Andrew “bunny” Huang deliver a lot more commentary on this threat and suggest some mitigations.

From the perspective of an OEM designing with FPGAs there are two elements to consider here:

  • The physical supply chain: How can you be sure you getting authentic parts, that have not been tampered with? Counterfeiting is on the rise – as seen in the CISCO example above.  Furthermore, with recent COVID-19 related chip shortages, experts are predicting a sharp rise in counterfeit components.
  • The ‘soft’ supply chain: The trend towards abstracting away the specifics of the RTL design, with higher level languages and readily available and easy-to-integrate logic blocks (also known as intellectual property (IP) blocks), continues to accelerate – but as this accelerates the trust in the third-parties providing these erodes. Do you know what facilitates their security? Open-source projects are not immune from this. Recently, unsuccessful attempts were made to test the ability of the Linux Kernel project, to react to so-called ‘hypocrite commits’. These commits included a small patch to fix a minor issue, but then knowingly introduced a more critical issue (in this case, a use-after-free vulnerability) than the fixed one. The full story, is well worth reading.

Obsolesence

Ironically, an FPGA’s flexibility could lead to a lack of attention being paid to obsolescence.

Let’s set a scene:

Startup Company A creates device X (built around an FPGA), and enjoys widespread success, with millions of units shipped worldwide. After several years, a design flaw is discovered in the FPGA design within device X.

For example, let’s consider that the FPGA used in device X was a Cyclone II device. This is still available (28th July 2021) from popular electronic part vendors. Yet, the last supported version of the design tools that support this device date from July 2013 – before Windows 10 was even released! When attempting to download this software the user is presented with a warning:

This archived version does not include the latest functional and security updates. For a supported version of Quartus, upgrade to the latest version

which, inevitably, requires the purchase and migration of the design to new silicon.

Arguably this creates a false sense of security; the common (and false) belief that “we can fix that after it’s shipped”. But if you are reliant on the vendor maintaining the software, and your ability to iterate those designs is compromised – and so we must ask, how long does that statement hold true?

Worse still, you must somehow maintain an old, and unsupported version of Windows if you want to guarantee that the tooling will still be compatible. This can leave the rest of your IT infrastructure vulnerable.

As the recent WD My Book Live attacks have shown, manufacturers often find maintenance of old devices either too challenging or economically unappealing, a problem that is possibly more difficult with reconfigurable hardware. For more detailed discussion of the right to repair, and what happens at end-of-life, see Rob Wood’s recent blog post on this subject.

Side channel and fault injection attacks

Traditionally, side-channel and fault injection attacks have always required physical access to the device – thus, keeping a device in a physically secured environment is a reasonable mitigation. With the proliferation of cloud-based computing, however, that paradigm changes.

A number of research papers exist that discuss the potential problems of a multi-tenanted FPGA, and the ability of an attacker to perform a side-channel attack against other logic in the device. Another piece of research, DeepStrike demonstrates a remote injection attack on a DNN accelerator on an FPGA.

These attacks, it must be noted – all rely on the attacker having access to or being co-tenanted on the same FPGA as another user. At time of writing, while some providers allow multiple tenants on the same FPGA host system, it is not clear whether multiple tenants are permitted to occupy the same FPGA within a commercial deployment.

A more unique, and potentially relevant piece of research entitled C3APSULe, practically demonstrates how an FPGA can be used to establish a covert channel between FPGA-FPGA, FPGA-CPU and FPGA-GPU, when co-located on the same power supply unit. This opens other unique avenues of attack on cloud-hosted environments. Cloud providers mitigate some of these issues by blocking combinatorial loops.

The traditional approach (where the attacker has access to the physical device) is changing too. As mentioned above, tools like Chipwhisperer are bringing side-channel power analysis and fault injection attacks to new audiences.

My colleagues Jeremy Boone and Sultan Qasim Khan, provide a strong primer to fault injection attacks (and defenses), which, like differential power analysis, is becoming more accessible and more common.

Real-World Attack Scenarios

With any complex system, it is important to consider the systems’ threat model. FPGA-based systems are no different. By way of example, for the purpose of this blog post we consider two simplistic scenarios – and only  consider the potential for remote attacks (i.e. without access to the physical hardware).

An FPGA used for compute offload

In this scenario, an FPGA is being used to offload compute effort from the host operating system (OS). For example, algorithmic calculations for a cryptocurrency. The FPGA is situated on a PCIe card, and communicates with the host application via a driver installed on the host OS.

Diagram

Description automatically generated
Offloading processing to FPGA

In this scenario, the FPGA is relatively shielded from direct attack. As part of a defence-in-depth methodology, we would typically expect:

  • The application presents appropriate access controls externally.
  • The driver is acting as the ‘gatekeeper’ to the FPGA. It will likely have a series of specific calls that it exposes to the host operating system (and applications running on it) via its API – limiting direct access to the FPGA.
  • The driver should perform extensive input validation on the data it receives from the application layer.
  • The FPGA is expected to perform sufficient validation on inputs, and to fail safe in the event of unexpected data from the driver, or the PCIe interface.
  • The host operating system to be appropriately hardened and secure. If this becomes compromised, then a logical assumption is that the FPGA will subsequently be compromised.
  • Appropriate validation is being performed on the bitfile being loaded by the FPGA to ensure it is as expected.
  • The FPGA workload/bitfile itself is not attacker controlled (if so, additional protections are needed to prevent the attacker from accessing the PCIe bus directly)

If any of the above fail to be met, the defence-in-depth approach becomes weaker – and the more gaps there are – then the lower the barrier to a complete compromise of the device. Depending on the application of the system, the results could range from the embarrassing (not being able to generate cryptocurrency) to the devasting (exfiltration or manipulation of completed hashes).

An FPGA used for pre-processing of network traffic

In this scenario, the FPGA is being used to perform pre-processing of network traffic prior to reaching the Host OS.

FPGA processing network trafic

In this instance, the FPGA is the front-line of defence and we are effectively almost considering the reverse of the above.

  •  The FPGA will need to validate all traffic it receives from the Ethernet port.
  • An in-house developed network stack will likely need substantial testing (including fuzzing) to validate its performance and security (formal verification may well be suitable in this scenario for identifying bugs).
  • If third-party or open-source IP block is used, examine the types of testing that have been performed and assess the cyber security
  • The driver is likely to implicitly trust the FPGA. Depending on the threat model of the system, this may need further validation efforts (for example, if an ‘evil maid’ or similar type of attack were a threat).
  • The application will need to verify and validate the data received from the FPGA
  •  The host operating system to be appropriately hardened and secure. If this becomes compromised, then a logical assumption is that the FPGA will subsequently be compromised.

In this example, the FPGA really is in the frontline of the defence. The high level of testing and input validation that the FPGA needs to perform will underpin the integrity of the rest of the system. Equally however, those components too need to be robust in order to get that defence in depth.

Conclusion

If you’ve made it this far, hopefully this blog post has provided you a better picture of the current state-of-play of the FPGA market, and some of the security challenges associated with them. A variety of innovative vulnerabilities have been discussed – each with different degrees of severity, ease of exploitability, and in some cases device proximity required.

The key to understanding the risk that your product or company faces when using an FPGA (or indeed any system) should be to start by defining and then understanding a threat model of your product.

Is the FPGA in your system in front-line of the system handling untrusted data? Or is it at the backend, accelerating processing on a known quantity? Is it possible you could be the target of high-effort attacks such as side-channel attacks, or do you feel that the barrier to entry for that type of attack is sufficiently high? And, if so is it likely to remain sufficiently high in the future as and when hardware attacks become further commoditised?

With that said, it is also important not to forget the fundamentals. The lowest hanging fruit are still the easiest for an attacker to target: both major vendors at the low-cost end of their ranges still sell silicon that doesn’t include bitstream protection at all.

Public Report – WhatsApp opaque-ke Cryptographic Implementation Review

13 December 2021 at 14:00

In June 2021, WhatsApp engaged NCC Group to conduct a security assessment of the ‘opaque-ke’ library, an open source Rust implementation of the OPAQUE password authenticated key exchange protocol. The protocol is designed to allow password-based authentication in such a way that a server does not actually learn the plaintext value of the client’s password, only a blinded version of the password computed using a verifiable oblivious pseudorandom function.

Two consultants spent a total of 15 days over 2 weeks focused on a detailed review of the ‘opaque-ke’ source code and the associated version of the OPAQUE specification. The library is open source, with code available in GitHub, and the specification is available as an IETF draft RFC. The WhatsApp team provided support throughout the engagement, and the NCC Group project team achieved good coverage of the provided source code and the associated draft of the OPAQUE specification.

The Public Report for this review may be downloaded below:

log4j-jndi-be-gone: A simple mitigation for CVE-2021-44228

12 December 2021 at 23:55

tl;dr Run our new tool by adding -javaagent:log4j-jndi-be-gone-1.0.0-standalone.jar to all of your JVM Java stuff to stop log4j from loading classes remotely over LDAP. This will prevent malicious inputs from triggering the “Log4Shell” vulnerability and gaining remote code execution on your systems. .

In this post, we first offer some context on the vulnerability, the released fixes (and their shortcomings), and finally our mitigation (or you can skip directly to our mitigation tool here).

Update (12/16/21): This post has been updated in line with new information regarding incomplete patches for CVE-2021-44228; CVE-2021-45046; weaknesses in the official mitigations; and the log4j release with updated fixes, 2.16.0.

Context: log4shell

Hello internet, it’s been a rough week. As you have probably learned, basically every Java app in the world uses a library called “log4j” to handle logging, and that any string passed into those logging calls will evaluate magic ${jndi:ldap://...} sequences to remotely load (malicious) Java class files over the internet (CVE-2021-44228, “Log4Shell”). Right now, while the SREs are trying to apply the not-quite-a-fix official fix and/or implement egress filtering without knocking their employers off the internet, most people are either blaming log4j for even having this JNDI stuff in the first place and/or blaming the issue on a lack of support for the project that would have helped to prevent such a dangerous behavior from being so accessible. In reality, the JNDI stuff is regrettably more of an “enterprise” feature than one that developers would just randomly put in if left to their own devices. Enterprise Java is all about antipatterns that invoke code in roundabout ways to the point of obfuscation, and supporting ever more dynamic ways to integrate weird protocols like RMI to load and invoke remote code dynamically in weird ways. Even the log4j format “Interpolator” wraps a bunch of handlers, including the JNDI handler, in reflection wrappers. So, if anything, more “(financial) support” for the project would probably just lead to more of these kinds of things happening as demand for one-off formatters for new systems grows among larger users. Welcome to Enterprise Java Land, where they’ve already added log4j variable expansion for Docker and Kubernetes. Alas, the real problem is that log4j 2.x (the version basically everyone uses) is designed in such a way that all string arguments after the main format string for the logging call are also treated as format strings. Basically all log4j calls are equivalent to if the following C:

printf("%s\n", "clobbering some bytes %n");

were implemented as the very unsafe code below:

char *buf;
asprintf(&buf, "%s\n", "clobbering some bytes %n");
printf(buf);

Basically, log4j never got the memo about format string vulnerabilities and now it’s (probably) too late. It was only a matter of time until someone realized they exposed a magic format string directive that led to code execution (and even without the classloading part, it is still a means of leaking expanded variables out through other JNDI-compatible services, like DNS), and I think it may only be a matter of time until another dangerous format string handler gets introduced into log4j. Meanwhile, even without JNDI, if someone has access to your log4j output (wherever you send it), and can cause their input to end up in a log4j call (pretty much a given based on the current havoc playing out) they can systematically dump all sorts of process and system state into it including sensitive application secrets and credentials. Had log4j not implemented their formatting this way, then the JNDI issue would only impact applications that concatenated user input into the format string (a non-zero amount, but much less than 100%).

The “Fixes”

The main fix is to update to the just released log4j 2.15.0 2.16.0. Prior to that the release of 2.15.0, the official mitigation from the log4j maintainers was:

“In releases >=2.10, this behavior can be mitigated by setting either the system property log4j2.formatMsgNoLookups or the environment variable LOG4J_FORMAT_MSG_NO_LOOKUPS to true. For releases from 2.0-beta9 to 2.10.0, the mitigation is to remove the JndiLookup class from the classpath: zip -q -d log4j-core-*.jar org/apache/logging/log4j/core/lookup/JndiLookup.class.”

Apache Log4j

So to be clear, the fix given for older versions of log4j (2.0-beta9 until 2.10.0) is to find and purge the JNDI handling class from all of your JARs, which are probably all-in-one fat JARs because no one uses classpaths anymore, all to prevent it from being loaded.

Then there is the fix for more recent versions of log4j (2.10.0 to 2.14.1), which is to set a magic Java system property (log4j2.formatMsgNoLookups) via the command line or weird XML shenanigans, or to add a magic environment variable (LOG4J_FORMAT_MSG_NO_LOOKUPS) to your Java processes (likely by reconfiguring your [systemd] service definitions). And you should be aware that both of these simply disable all ${} handling. Lastly, the real prior fix is was to ‘just update it’ to the new version (2.15.0) that defaults to the functionality being turned off in an overly complicated patch that… removes the formatMsgNoLookups handling, but sets the default to true? They apparently changed the handling so that lookup handlers are opt-in, enabled by XML-based configuration, a change so cumbersome I question how long it will last.


Update (12/16/21): As it turns out, there was an edge case left in 2.15.0 enabling denial-of-service attacks (CVE-2021-45046) — but also RCE? (see below) — against log4j message processing. Due to the removal of the formatMsgNoLookups/LOG4J_FORMAT_MSG_NO_LOOKUPS handling, which otherwise would have prevented any lookup based logic from triggering, 2.15.0’s denial-of-service vulnerability cannot be mitigated with that setting (as it holds no meaning in that version).

Additionally, the formatMsgNoLookups/LOG4J_FORMAT_MSG_NO_LOOKUPS-based mitigation does not appear to provide protection in cases where the layout pattern is customized to include “ContextMap” lookups (e.g. via "${ctx:...}") and application/library code enables users to configure org.apache.logging.log4j.ThreadContext values (or custom org.apache.logging.log4j.core.ContextDataInjector and possibly org.apache.logging.log4j.core.util.ContextDataProvider implementations), such as via calls to ThreadContext.put().

Lastly, one of the more esoteric official recommendations that has since been removed from the log4j website, was the following: “Users since Log4j 2.7 may specify %m{nolookups} in the PatternLayout configuration to prevent lookups in log event messages.” Additional research on the official mitigations done by LunaSec has shown that %m{nolookups} will only apply to the content being logged in the call to logger.info() and will not apply to the resolving of ${ctx:apiversion} which will contain our payload” (emphasis ours).

This is interesting because the explanation for the “new” vulnerability, CVE-2021-45046, is the following, which describes only a denial-of-service issue:

It was found that the fix to address CVE-2021-44228 in Apache Log4j 2.15.0 was incomplete in certain non-default configurations. This could allows attackers with control over Thread Context Map (MDC) input data when the logging configuration uses a Pattern Layout with either a Context Lookup (for example, $${ctx:loginId}) or a Thread Context Map pattern (%X, %mdc, or %MDC) to craft malicious input data using a JNDI Lookup pattern resulting in a denial of service (DOS) attack. Log4j 2.15.0 restricts JNDI LDAP lookups to localhost by default.

Apache Log4j

The denial-of-service listed appears to be the potential for infinite loops whereby an controlled ThreadContext value self-references itself. Based on the stack trace provided by LunaSec, it appears that at least simple self-references are detected by Log4j, which raises a java.lang.IllegalStateException a type of RuntimeException that would generally raise until a catch-all exception handler. While this is technically a denial-of-service, had the exact conditions for it not also enabled code execution as outlined above, it would have only presented a risk to code that assumed there would be no uncaught exceptions.


None of these solutions are ideal because they all presume that everyone has a 100% handle on their dependency chains and that you won’t end up with a mix of log4j versions strewn across the subcomponents of apps segmented across multiple classloaders (looking at you, WAR files). The reality is that the node_modules-style of dependency embedding is not completely alien to the Java ecosystem (and Java has a host of left-pad type issues due to how its build systems resolve dependencies across multiple repositories). In addition to this, many apps and proprietary libraries and app servers possibly vendor-in log4j in weird ways that end up preferring their own versions over any shared version that might actually be updated by your ops team. All told, tracking down and updating every instance of a library like this in your application stack is a nontrivial process and potentially error prone as any one rogue log4j copy can potentially keep a service vulnerable.

A tool to mitigate Log4Shell by disabling log4j JNDI

To try to make the situation a little bit more manageable in the meantime, we are releasing log4j-jndi-be-gone, a dead simple Java agent that disables the log4j JNDI handler outright. log4j-jndi-be-gone uses the Byte Buddy bytecode manipulation library to modify the at-issue log4j class’s method code and short circuit the JNDI interpolation handler. It works by effectively hooking the at-issue JndiLookup class’ lookup() method that Log4Shell exploits to load remote code, and forces it to stop early without actually loading the Log4Shell payload URL. It also supports Java 6 through 17, covering older versions of log4j that support Java 6 (2.0-2.3) and 7 (2.4-2.12.1), and works on read-only filesystems (once installed or mounted) such as in read-only containers.

Update (12/16/21): Due to the way it works, log4j-jndi-be-gone will prevent any JNDI lookups, including Thread Context Map-based ones that still impact log4j 2.15.0, but it does not prevent the limited “denial-of-service” attack against that version under such configurations.

The benefit of this Java agent is that a single command line flag can negate the vulnerability regardless of which version of log4j is in use, so long as it isn’t obfuscated (e.g. with proguard), in which case you may not be in a good position to update it anyway. log4j-jndi-be-gone is not a replacement for the -Dlog4j2.formatMsgNoLookups=true system property in supported versions, but helps to deal with those older versions that don’t support it.

Using it is pretty simple, just add -javaagent:path/to/log4j-jndi-be-gone-1.0.0-standalone.jar to your Java commands. In addition to disabling the JNDI handling, it also prints a message indicating that a log4j JNDI attempt was made with a simple sanitization applied to the URL string to prevent it from becoming a propagation vector. It also “resolves” any JNDI format strings to "(log4j jndi disabled)" making the attempts a bit more grep-able.

$ java -javaagent:log4j-jndi-be-gone-1.0.0.jar -jar myapp.jar

log4j-jndi-be-gone is available from our GitHub repo, https://github.com/nccgroup/log4j-jndi-be-gone. You can grab a pre-compiled log4j-jndi-be-gone agent JAR from the releases page, or build one yourself with ./gradlew, assuming you have a recent version of Java installed.

Log4Shell: Reconnaissance and post exploitation network detection

Note: This blogpost will be live-updated with new information. NCC Group’s RIFT is intending to publish PCAPs of different exploitation methods in the near future – last updated December 15th at 17:30 UTC

tl;dr

In the wake of the CVE-2021-44228, CVE-2021-45046 and CVE-2021-44832 (a.k.a. Log4Shell) vulnerability publication, NCC Group’s RIFT immediately started investigating the vulnerability in order to improve detection and response capabilities mitigating the threat.

This blog post is focused on detection and threat hunting, although attack surface scanning and identification are also quintessential parts of a holistic response. Multiple references for prevention and mitigation can be found included at the end of this post.

This blogpost provides Suricata network detection rules that can be used not only to detect exploitation attempts, but also indications of successful exploitation. In addition, a list of indicators of compromise (IOC’s) are provided. These IOC’s have been observed listening for incoming connections and are thus a useful for threat hunting.

Update Wednesday December 29th, 09:00 UTC

CVE-2021-44832

A further vulnerability was disclosed on December 28th and is tracked under CVE-2021-44832. NCC Group assesses this vulnerability to be lower priority than the original due to the requirement of pre-existing privileged access to underlying hosts in order to exploit.

From the disclosure:

where an attacker with permission to modify the logging configuration file can construct a malicious configuration using a JDBC Appender with a data source referencing a JNDI URI which can execute remote code.

This vulnerability may be used as a persistence technique but is unlikely to be used as an initial entry mechanism due to the need to modify configuration.

Update Friday December 24th, 14:50 UTC

Log4Shell PCAPS and Network Coverage

Since the publication of the Log4Shell exploit there have been a lot of developments surrounding the Log4j CVE, leading to several new versions of the package to fix the workarounds that people found for the mitigations. During this time, there were also many people focusing their efforts on finding evasive methods to bypass mitigations put in place that block exploitation by monitoring for the exploitation string.

Because of the variety of the evasive methods, and the different protocols that can be used to exploit the vulnerability, we have created pcaps and an overview to assist security engineers in their endeavours to check their current detection coverage.

Setup

RIFT has used an environment to test different scenarios with the purpose of automatically creating pcaps and testing network coverage for the Remote Code Execution (RCE) vectors of Log4Shell using LDAP and RMI.

We tested different vectors that attackers could use in real-world scenarios, focusing on the HTTP protocol as this has been observed being used in the wild. Please keep in mind that HTTP is by no means the only protocol attackers can use to trigger the vulnerability in applications using a vulnerable version of Log4j. Any string that is logged by a vulnerable Log4j is subject to exploitation. We have also seen different evasion techniques, so these have also been tested for coverage.

We want to emphasize that we already observed attackers using encoded variants of the available protocols (HTTP Basic Authorization) and that there are plentiful other encoding methods that might still be logged decoded by the application using a vulnerable Log4j package.

We have used the following tools for testing the exploitation:

For web applications that is vulnerable to log4shell we have used:

Log4Shell PCAPs and Coverage Tracking

The tables displayed below give an overview of the different evasion methods and their respective coverage. The PCAP filenames contain the ev string to mark the evasion ID.

EV Payload
1 ${${::-j}${::-n}${::-d}${::-i}:${::-l}${::-d}${::-a}${::-p}://
${${::-j}${::-n}${::-d}${::-i}:${::-r}${::-m}${::-i}://
2 ${${lower:jndi}:${lower:ldap}://
${${::-j}ndi:rmi://
3 ${${lower:${lower:jndi}}:${lower:ldap}://
${${lower:jndi}:${lower:rmi}://
4 ${${lower:j}${lower:n}${lower:d}i:${lower:ldap}://
${${lower:${lower:jndi}}:${lower:rmi}://
5 ${${lower:j}${upper:n}${lower:d}${upper:i}:${lower:l}d${lower:a}p://
${${lower:j}${upper:n}${lower:d}${upper:i}:${lower:r}m${lower:i}://
6 ${j${env:DOESNOTEXIST:-}ndi:ldap://
${j${env:DOESNOTEXIST:-}ndi:rmi://
7 ${${: : : : ::: :: :: : :::-j}ndi:ldap://
${${: : : : ::: :: :: : :::-j}ndi:rmi://
8 ${${::::::::::::::-j}ndi:ldap://
${${::::::::::::::-j}ndi:rmi://

PCAPS etc. can be found here https://github.com/fox-it/log4shell-pcaps

Signatures

Our Log4Shell Suricata signatures can be found here: log4shell-suricata.rules

We have found that our signatures for outgoing LDAP and RMI packets are the best indicators (sids 21003738 and 21003739) of detecting a successful Log4Shell detonation. This also covers the situation where the malicious JNDI string is not always detected, for example due to TLS, but the IDS still monitors outgoing traffic.

Furthermore, the exploit chain itself might not always succeed, for example, due to Java versions or hardening of the system and or network. However, when these signatures trigger, a vulnerable Log4j version performed the callback and should be further investigated to determine which application caused it.

Update Wednesday December 15th, 17:30 UTC

We have seen 5 instances in our client base of active exploitation of Mobile Iron during the course of yesterday and today.

Our working hypothesis is that this is a derivative of the details shared yesterday – https://github.com/rwincey/CVE-2021-44228-Log4j-Payloads/blob/main/MobileIron.

The scale of the exposure globally appears significant

We recommend all Mobile Iron users updated immediately.

Ivanti informed us that communication was sent over the weekend to MobileIron Core customers. Ivanti has provided mitigation steps of the exploit listed below on their Knowledge Base. Both NCC Group and Ivanti recommend all customers immediately apply the mitigation within to ensure their environment is protected.

Update Tuesday December 14th, 13:00 UTC

Log4j-finder: finding vulnerable versions of Log4j on your systems

RIFT has published a Python 3 script that can be run on endpoints to check for the presence of vulnerable versions of Log4j. The script requires no dependencies and supports recursively checking the filesystem and inside JAR files to see if they contain a vulnerable version of Log4j. This script can be of great value in determining which systems are vulnerable, and where this vulnerability stems from. The script will be kept up to date with ongoing developments.

It is strongly recommended to run host based scans for vulnerable Log4j versions. Whereas network-based scans attempt to identify vulnerable Log4j versions by attacking common entry points, a host-based scan can find Log4j in unexpected or previously unknown places.

The script can be found on GitHub: https://github.com/fox-it/log4j-finder

JNDI ExploitKit exposes larger attack surface

As shown by the release of an update JNDI ExploitKIT  it is possible to reach remote code execution through serialized payloads instead of referencing a Java .class object in LDAP and subsequently serving that to the vulnerable system. While TrustURLCodebase defaults to false in newer Java versions (6u211, 7u201, 8u191, and 11.0.1) and therefore prevents the LDAP reference vector,depending on the loaded libraries in the vulnerable application it is possible to execute code through Java serialization via both rmi and ldap.

Beware: Centralized logging can result in indirect compromise

This is also highly relevant for organisations using a form of centralised logging. Centralised logging can be used to collect and parse the received logs from the different services and applications running in the environment. We have identified cases where a Kibana server was not exposed to the Internet but because it received logs from several appliances it still got hit by the Log4Shell RCE and started to retrieve Java objects via LDAP.

We were unable to determine if this was due to Logstash being used in the background for parsing the received logs, but this stipulates the importance of checking systems configured with centralised logging solutions for vulnerable versions of Log4j, and not rely on the protection of newer JDK versions that has com.sun.jndi.ldap.object.trustURLCodebase
com.sun.jndi.rmi.object.trustURLCodebase set to false by default.

A warning concerning possible post-exploitation

It is therefore advised to apply the patches provided by Microsoft in the November 2021 security updateAlthough largely eclipsed by Log4Shell, last weekend also saw the emergence of details concerning two vulnerabilities (CVE-2021-42287 and CVE-2021-42278) that reside in the Active Directory component of Microsoft Windows Server editions. Due to the nature of these vulnerabilities, an attackers could escalate their privileges in a relatively easy manner as these vulnerabilities have already been weaponised.

It is therefore advised to apply the patches provided by Microsoft in the November 2021 security updates to every domain controller that is residing in the network as it is a possible form of post-exploitation after Log4Shell were to be successfully exploited.

Background

Since Log4J is used by many solutions there are significant challenges in finding vulnerable systems and any potential compromise resulting from exploitation of the vulnerability. JNDI (Java Naming and Directory Interface™) was designed to allow distributed applications to look up services in a resource-independent manner, and this is exactly where the bug resulting in exploitation resides. The nature of JNDI allows for defense-evading exploitation attempts that are harder to detect through signatures. An additional problem is the tremendous amount of scanning activity that is currently ongoing. Because of this, investigating every single exploitation attempt is in most situations unfeasible. This means that distinguishing scanning attempts from actual successful exploitation is crucial.

In order to provide detection coverage for CVE-2021-44228 and CVE-2021-45046, NCC Group’s RIFT first created a ruleset that covers as many ways as possible of attempted exploitation of the vulnerability. This initial coverage allowed the collection of Threat Intelligence for further investigation. Most adversaries appear to use a different IP to scan for the vulnerability than they do for listening for incoming victim machines. IOC’s for listening IP’s / domains are more valuable than those of scanning IP’s. After all a connection from an environment to a known listening IP might indicate a successful compromise, whereas a connection to a scanning IP might merely mean that it has been scanned.

After establishing this initial coverage, our focus shifted to detecting successful exploitation in real time. This can be done by monitoring for rogue JRMI or LDAP requests to external servers. Preferably, this sort of behavior is detected in a port-agnostic way as attackers may choose arbitrary ports to listen on. Moreover, currently a full RCE chain requires the victim machine to retrieve a Java class file from a remote server (caveat: unless exfiltrating sensitive environment variables). For hunting purposes we are able to hunt for inbound Java classes. However, if coverage exists for incoming attacks we are also able to alert on an inbound Java class in a short period of time after an exploitation attempt. The combination of inbound exploitation attempt and inbound Java class is a high confidence IOC that a successful connection has occurred.

This blogpost will continue twofold: we will first provide a set of suricata rules that can be used for:

  1. Detecting incoming exploitation attempts;
  2. Alerting on higher confidence indicators that successful exploitation has occurred;
  3. Generating alerts that can be used for hunting

After providing these detection rules, a list of IOC’s is provided.

Detection Rules

Some of these rules are redundant, as they’ve been written in rapid succession.

# Detects Log4j exploitation attempts
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Apache Log4J RCE Request Observed (CVE-2021-44228)"; flow:established, to_server; content:"${jndi:ldap://"; fast_pattern:only; flowbits:set, fox.apachelog4j.rce; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:web-application-attack; priority:3; reference:url, http://www.lunasec.io/docs/blog/log4j-zero-day/; metadata:CVE 2021-44228; metadata:created_at 2021-12-10; metadata:ids suricata; sid:21003726; rev:1;)
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Apache Log4J RCE Request Observed (CVE-2021-44228)"; flow:established, to_server; content:"${jndi:"; fast_pattern; pcre:"/\$\{jndi\:(rmi|ldaps|dns)\:/"; flowbits:set, fox.apachelog4j.rce; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:web-application-attack; priority:3; reference:url, http://www.lunasec.io/docs/blog/log4j-zero-day/; metadata:CVE 2021-44228; metadata:created_at 2021-12-10; metadata:ids suricata; sid:21003728; rev:1;)
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Defense-Evasive Apache Log4J RCE Request Observed (CVE-2021-44228)"; flow:established, to_server; content:"${jndi:"; fast_pattern; content:!"ldap://"; flowbits:set, fox.apachelog4j.rce; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:web-application-attack; priority:3; reference:url, http://www.lunasec.io/docs/blog/log4j-zero-day/; reference:url, twitter.com/stereotype32/status/1469313856229228544; metadata:CVE 2021-44228; metadata:created_at 2021-12-10; metadata:ids suricata; sid:21003730; rev:1;)
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Defense-Evasive Apache Log4J RCE Request Observed (URL encoded bracket) (CVE-2021-44228)"; flow:established, to_server; content:"%7bjndi:"; nocase; fast_pattern; flowbits:set, fox.apachelog4j.rce; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:web-application-attack; priority:3; reference:url, http://www.lunasec.io/docs/blog/log4j-zero-day/; reference:url, https://twitter.com/testanull/status/1469549425521348609; metadata:CVE 2021-44228; metadata:created_at 2021-12-11; metadata:ids suricata; sid:21003731; rev:1;)
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Apache Log4j Exploit Attempt in HTTP Header"; flow:established, to_server; content:"${"; http_header; fast_pattern; content:"}"; http_header; distance:0; flowbits:set, fox.apachelog4j.rce.loose; classtype:web-application-attack; priority:3; threshold:type limit, track by_dst, count 1, seconds 3600; reference:url, http://www.lunasec.io/docs/blog/log4j-zero-day/; reference:url, https://twitter.com/testanull/status/1469549425521348609; metadata:CVE 2021-44228; metadata:created_at 2021-12-11; metadata:ids suricata; sid:21003732; rev:1;)
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Apache Log4j Exploit Attempt in URI"; flow:established,to_server; content:"${"; http_uri; fast_pattern; content:"}"; http_uri; distance:0; flowbits:set, fox.apachelog4j.rce.loose; classtype:web-application-attack; priority:3; threshold:type limit, track by_dst, count 1, seconds 3600; reference:url, http://www.lunasec.io/docs/blog/log4j-zero-day/; reference:url, https://twitter.com/testanull/status/1469549425521348609; metadata:CVE 2021-44228; metadata:created_at 2021-12-11; metadata:ids suricata; sid:21003733; rev:1;)
# Better and stricter rules, also detects evasion techniques
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Apache Log4j Exploit Attempt in HTTP Header (strict)"; flow:established,to_server; content:"${"; http_header; fast_pattern; content:"}"; http_header; distance:0; pcre:/(\$\{\w+:.*\}|jndi)/Hi; xbits:set, fox.log4shell.attempt, track ip_dst, expire 1; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:web-application-attack; reference:url,www.lunasec.io/docs/blog/log4j-zero-day/; reference:url,https://twitter.com/testanull/status/1469549425521348609; metadata:CVE 2021-44228; metadata:created_at 2021-12-11; metadata:ids suricata; priority:3; sid:21003734; rev:1;)
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Apache Log4j Exploit Attempt in URI (strict)"; flow:established, to_server; content:"${"; http_uri; fast_pattern; content:"}"; http_uri; distance:0; pcre:/(\$\{\w+:.*\}|jndi)/Ui; xbits:set, fox.log4shell.attempt, track ip_dst, expire 1; classtype:web-application-attack; threshold:type limit, track by_dst, count 1, seconds 3600; reference:url,www.lunasec.io/docs/blog/log4j-zero-day/; reference:url,https://twitter.com/testanull/status/1469549425521348609; metadata:CVE 2021-44228; metadata:created_at 2021-12-11; metadata:ids suricata; priority:3; sid:21003735; rev:1;)
alert http any any -> $HOME_NET any (msg:"FOX-SRT – Exploit – Possible Apache Log4j Exploit Attempt in Client Body (strict)"; flow:to_server; content:"${"; http_client_body; fast_pattern; content:"}"; http_client_body; distance:0; pcre:/(\$\{\w+:.*\}|jndi)/Pi; flowbits:set, fox.apachelog4j.rce.strict; xbits:set,fox.log4shell.attempt,track ip_dst,expire 1; classtype:web-application-attack; threshold:type limit, track by_dst, count 1, seconds 3600; reference:url,www.lunasec.io/docs/blog/log4j-zero-day/; reference:url,https://twitter.com/testanull/status/1469549425521348609; metadata:CVE 2021-44228; metadata:created_at 2021-12-12; metadata:ids suricata; priority:3; sid:21003744; rev:1;)

Detecting outbound connections to probing services

Connections to outbound probing services could indicate a system in your network has been scanned and subsequently connected back to a listening service. This could indicate that a system in your network is/was vulnerable and has been scanned.

# Possible successful interactsh probe
alert http $EXTERNAL_NET any -> $HOME_NET any (msg:"FOX-SRT – Webattack – Possible successful InteractSh probe observed"; flow:established, to_client; content:"200"; http_stat_code; content:"<html><head></head><body>"; http_server_body; fast_pattern; pcre:"/[a-z0-9]{30,36}<\/body><\/html>/QR"; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:misc-attack; reference:url, github.com/projectdiscovery/interactsh; metadata:created_at 2021-12-05; metadata:ids suricata; priority:2; sid:21003712; rev:1;)
alert dns $HOME_NET any -> any 53 (msg:"FOX-SRT – Suspicious – DNS query for interactsh.com server observed"; flow:stateless; dns_query; content:".interactsh.com"; fast_pattern; pcre:"/[a-z0-9]{30,36}\.interactsh\.com/"; threshold:type limit, track by_src, count 1, seconds 3600; reference:url, github.com/projectdiscovery/interactsh; classtype:bad-unknown; metadata:created_at 2021-12-05; metadata:ids suricata; priority:2; sid:21003713; rev:1;)
# Detecting DNS queries for dnslog[.]cn
alert dns any any -> any 53 (msg:"FOX-SRT – Suspicious – dnslog.cn DNS Query Observed"; flow:stateless; dns_query; content:"dnslog.cn"; fast_pattern:only; threshold:type limit, track by_src, count 1, seconds 3600; classtype:bad-unknown; metadata:created_at 2021-12-10; metadata:ids suricata; priority:2; sid:21003729; rev:1;)
# Connections to requestbin.net
alert dns $HOME_NET any -> any 53 (msg:"FOX-SRT – Suspicious – requestbin.net DNS Query Observed"; flow:stateless; dns_query; content:"requestbin.net"; fast_pattern:only; threshold:type limit, track by_src, count 1, seconds 3600; classtype:bad-unknown; metadata:created_at 2021-11-23; metadata:ids suricata; sid:21003685; rev:1;)
alert tls $HOME_NET any -> $EXTERNAL_NET 443 (msg:"FOX-SRT – Suspicious – requestbin.net in SNI Observed"; flow:established, to_server; tls_sni; content:"requestbin.net"; fast_pattern:only; threshold:type limit, track by_src, count 1, seconds 3600; classtype:bad-unknown; metadata:created_at 2021-11-23; metadata:ids suricata; sid:21003686; rev:1;)

Detecting possible successful exploitation

Outbound LDAP(S) / RMI connections are highly uncommon but can be caused by successful exploitation. Inbound Java can be suspicious, especially if it is shortly after an exploitation attempt.

# Detects possible successful exploitation of Log4j
# JNDI LDAP/RMI Request to External
alert tcp $HOME_NET any -> $EXTERNAL_NET any (msg:"FOX-SRT – Exploit – Possible Rogue JNDI LDAP Bind to External Observed (CVE-2021-44228)"; flow:established, to_server; dsize:14; content:"|02 01 03 04 00 80 00|"; offset:7; isdataat:!1, relative; threshold:type limit, track by_src, count 1, seconds 3600; classtype:bad-unknown; priority:1; metadata:created_at 2021-12-11; sid:21003738; rev:2;)
alert tcp $HOME_NET any -> $EXTERNAL_NET any (msg:"FOX-SRT – Exploit – Possible Rogue JRMI Request to External Observed (CVE-2021-44228)"; flow:established, to_server; content:"JRMI"; depth:4; threshold:type limit, track by_src, count 1, seconds 3600; classtype:bad-unknown; priority:1; reference:url, https://docs.oracle.com/javase/9/docs/specs/rmi/protocol.html; metadata:created_at 2021-12-11; sid:21003739; rev:1;)
# Detecting inbound java shortly after exploitation attempt
alert tcp any any -> $HOME_NET any (msg: "FOX-SRT – Exploit – Java class inbound after CVE-2021-44228 exploit attempt (xbit)"; flow:established, to_client; content: "|CA FE BA BE 00 00 00|"; depth:40; fast_pattern; xbits:isset, fox.log4shell.attempt, track ip_dst; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:successful-user; priority:1; metadata:ids suricata; metadata:created_at 2021-12-12; sid:21003741; rev:1;)

Hunting rules (can yield false positives)

Wget and cURL to external hosts was observed to be used by an actor for post-exploitation. As cURL and Wget are also used legitimately, these rules should be used for hunting purposes. Also note that attackers can easily change the User-Agent but we have not seen that in the wild yet. Outgoing connections after Log4j exploitation attempts can be tracked to be later hunted on although this rule can generate false positives if victim machine makes outgoing connections regularly. Lastly, detecting inbound compiled Java classes can also be used for hunting.

# Outgoing connection after Log4j Exploit Attempt (uses xbit from sid: 21003734) – requires `stream.inline=yes` setting in suricata.yaml for this to work
alert tcp $HOME_NET any -> $EXTERNAL_NET any (msg:"FOX-SRT – Suspicious – Possible outgoing connection after Log4j Exploit Attempt"; flow:established, to_server; xbits:isset, fox.log4shell.attempt, track ip_src; stream_size:client, =, 1; stream_size:server, =, 1; threshold:type limit, track by_dst, count 1, seconds 3600; classtype:bad-unknown; metadata:ids suricata; metadata:created_at 2021-12-12; priority:3; sid:21003740; rev:1;)
# Detects inbound Java class
alert tcp $EXTERNAL_NET any -> $HOME_NET any (msg: "FOX-SRT – Suspicious – Java class inbound"; flow:established, to_client; content: "|CA FE BA BE 00 00 00|"; depth:20; fast_pattern; threshold:type limit, track by_dst, count 1, seconds 43200; metadata:ids suricata; metadata:created_at 2021-12-12; classtype:bad-unknown; priority:3; sid:21003742; rev:2;)

Indicators of Compromise

This list contains Domains and IP’s that have been observed to listen for incoming connections. Unfortunately, some adversaries scan and listen from the same IP, generating a lot of noise that can make threat hunting more difficult. Moreover, as security researchers are scanning the internet for the vulnerability as well, it could be possible that an IP or domain is listed here even though it is only listening for benign purposes.

# IP addresses and domains that have been observed in Log4j exploit attempts
134[.]209[.]26[.]39
199[.]217[.]117[.]92
pwn[.]af
188[.]120[.]246[.]215
kryptoslogic-cve-2021-44228[.]com
nijat[.]space
45[.]33[.]47[.]240
31[.]6[.]19[.]41
205[.]185[.]115[.]217
log4j[.]kingudo[.]de
101[.]43[.]40[.]206
psc4fuel[.]com
185[.]162[.]251[.]208
137[.]184[.]61[.]190
162[.]33[.]177[.]73
34[.]125[.]76[.]237
162[.]255[.]202[.]246
5[.]22[.]208[.]77
45[.]155[.]205[.]233
165[.]22[.]213[.]147
172[.]111[.]48[.]30
133[.]130[.]120[.]176
213[.]156[.]18[.]247
m3[.]wtf
poc[.]brzozowski[.]io
206[.]188[.]196[.]219
185[.]250[.]148[.]157
132[.]226[.]170[.]154
flofire[.]de
45[.]130[.]229[.]168
c19s[.]net
194[.]195[.]118[.]221
awsdns-2[.]org
2[.]56[.]57[.]208
158[.]69[.]204[.]95
45[.]130[.]229[.]168
163[.]172[.]157[.]143
45[.]137[.]21[.]9
bingsearchlib[.]com
45[.]83[.]193[.]150
165[.]227[.]93[.]231
yourdns[.]zone[.]here
eg0[.]ru
dataastatistics[.]com
log4j-test[.]xyz
79[.]172[.]214[.]11
152[.]89[.]239[.]12
67[.]205[.]191[.]102
ds[.]Rce[.]ee
38[.]143[.]9[.]76
31[.]191[.]84[.]199
143[.]198[.]237[.]19

# (Ab)use of listener-as-a-service domains.
# These domains can be false positive heavy, especially if these services are used legitimately within your network.
interactsh[.]com
interact[.]sh
burpcollaborator[.]net
requestbin[.]net
dnslog[.]cn
canarytokens[.]com

# This IP is both a listener and a scanner at the same time. Threat hunting for this IOC thus requires additional steps.
45[.]155[.]205[.]233
194[.]151[.]29[.]154
158[.]69[.]204[.]95
47[.]254[.]127[.]78

References

General references

Mitigation:

Attack surface:

Known vulnerable services / products which use log4j:

Hashes of vulnerable products:

Announcing NCC Group’s Cryptopals Guided Tour!

10 December 2021 at 10:00

Hello and welcome to NCC Group’s Cryptopals guided tour! This post is the first in a series of eight installments covering the solutions to the Cryptopals Crypto Challenges. These have been a long time coming, and we’re excited to finally start bringing them to you.

For those who don’t know, Cryptopals is a series of eight sets of challenges covering common cryptographic constructs and how they can be attacked. You can read more about Cryptopals at https://cryptopals.com/.

There’s a lot of practical knowledge wrapped up in these challenges, and working through them is an excellent way for programmers to learn more about cryptography – or for cryptographers to learn more about programming. We strongly encourage you to give them a try and to see how far you can get on your own.

The series is meant as something you can check after finishing a challenge or set. It’s an opportunity to see how else you might’ve solved the challenges, to check for anything you might’ve missed, and to see the sights surrounding each problem. This is what we mean by “guided tour”: more than just a walkthrough, it’s a walk through the scenic route!

Of course, we can’t deliver on that promise through code alone. We tried writing up blog posts, but they started to run long and ended up looking more intimidating than they really were.

The format we finally settled on is screencasts: for each challenge, we’re releasing a recording of yours truly (Eli Sohl from NCC Group’s Cryptography Services practice) solving the challenge live and narrating the process from start to finish. Each video comes with a timestamped index of content, plus a list of links for further reading. We’ll release these videos one set at a time, starting with this post which covers the first set of challenges.

So, without further ado, here are the videos for Cryptopals Set 1. We hope you find them helpful, and we look forward to sharing the videos for the following challenge sets as soon as they’re ready.

Oh, and by the way: if you just want to see the finished solution code, you can find that here.

Set 1, Challenge 1: Convert hex to base64

Challenge link: https://cryptopals.com/sets/1/challenges/1

00:00 – Intro
01:10 – Exploring options for hex-decoding in an interpreter
02:00 – Introducing b16decode
03:15 – Writing the script’s main logic
04:00 – Format string f”{expr=}” syntax
04:50 – Testing the solution
05:00 – Cleaning up the solution by refactoring
06:00 – Adding a __name__ guard (and explaining why we need one)
07:00 – Adding an explicit output check and testing the solution again

Further reading:
https://www.ietf.org/rfc/rfc4648.txt
https://docs.python.org/3/library/base64.html
https://docs.python.org/3/reference/import.html?highlight=name#name__

Set 1, Challenge 2: Fixed XOR

Challenge link: https://cryptopals.com/sets/1/challenges/2

00:00 – Intro
01:00 – Iterating over two bytestrings in tandem
01:50 – Iterating by index
02:45 – Iterating by zip
03:40 – How to benchmark a function in iPython; benchmarking index and zip methods
04:50 – Implementing xor (first method: appending to bytestring)
06:48 – Implementing xor (second method: appending to list)
07:20 – Implementing xor (third method: joining bytes from a generator expression)
08:16 – Implementing xor (fourth method: calling bytes() on a generator expression)
08:47 – Benchmarking xor implementations
10:10 – Starting the solution script
10:55 – Wrapping two-argument bytes_xor, adding more features
13:47 – When to use assertions in Python (and when not to)
15:35 – Writing the main block
17:00 – Testing the solution

Further reading:
https://en.wikipedia.org/wiki/Exclusive_or#Computer_science
https://docs.python.org/3/library/functions.html#zip
https://www.python.org/dev/peps/pep-0289/
https://docs.python.org/3/glossary.html#term-generator
https://docs.python.org/3/using/cmdline.html#cmdoption-O
https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit

Set 1, Challenge 3: Single-byte XOR cipher

Challenge link: https://cryptopals.com/sets/1/challenges/3

00:00 – Intro
00:35 – Describing frequency analysis
01:10 – Downloading a book from Project Gutenberg and counting character frequencies
03:20 – Looking at frequency counts by character set
04:10 – Starting the solution script, talking about Black (“The uncompromising Python code formatter”)
04:45 – Writing a function to score a plaintext’s plausibility by its frequency counts
06:58 – Writing the XOR cipher cracking function (first version)
07:55 – How (and why) to use infinity in Python
10:30 – Writing crack_xor_cipher_worse so we can view top 5 candidate plaintexts
11:20 – Testing the cracking function
12:05 – Talking about ASCII table layout
13:37 – Talking about sample sizes, swapping out frequency count dictionary
15:20 – Modifying “score_text” to only count lowercase frequencies
15:45 – Solving the challenge, starting to refactor
16:20 – Introducing dataclasses and creating ScoredGuess dataclass
20:30 – Rewriting crack_xor_cipher to use ScoredGuess
23:12 – Optimizing the scoring algorithm
27:30 – Testing and benchmarking the optimized algorithm (it works really well!)

Further reading:
https://en.wikipedia.org/wiki/Substitution_cipher
https://en.wikipedia.org/wiki/Frequency_analysis
https://www.python.org/dev/peps/pep-0008/
https://github.com/psf/black
https://en.wikipedia.org/wiki/Etaoin_shrdlu
https://en.wikipedia.org/wiki/ASCII#Design_considerations
https://docs.python.org/3/library/dataclasses.html
https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit

Set 1, Challenge 4: Detect Single-Character XOR

Challenge link: https://cryptopals.com/sets/1/challenges/4

00:00 – Intro
00:30 – Explaining the solution strategy
01:00 – Starting the solution script
02:05 – Writing the search loop and solving the challenge
02:50 – Cleaning up the script
03:25 – Explaining how the “progress meter” works
05:23 – Retesting the solution

Further reading:
https://en.wikipedia.org/wiki/Distinguishing_attack

Set 1, Challenge 5: Implement repeating-key XOR

Challenge link: https://cryptopals.com/sets/1/challenges/5

00:00 – Intro
00:35 – Starting the solution script and explaining how it will work
01:00 – Talking about itertools
02:10 – Writing a main block (at hyperspeed)
02:25 – Talking about string literal concatenation
03:55 – Testing the solution

Further reading:
https://docs.python.org/3/library/itertools.html
https://docs.python.org/3/glossary.html#term-slice
https://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher
https://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher#Kasiski_examination
https://docs.python.org/3/reference/lexical_analysis.html#string-literal-concatenation

Set 1, Challenge 6: Break repeating-key XOR

Challenge link: https://cryptopals.com/sets/1/challenges/6

00:00 – Intro
00:45 – High-level overview of solution algorithm
01:30 – Talking about Hamming distance and Hamming weight
03:10 – Implementing Hamming weight (first method: converting to string, counting “1”s)
03:57 – Implementing Hamming weight (second method: checking lowest bit and shifting)
05:38 – Implementing Hamming weight (third method: fancy in-place digit sum)
12:10 – Implementing Hamming weight (fourth method: using subtraction to zero each 1 bit)
15:17 – Implementing Hamming weight (fifth method: lookup table)
16:02 – Implementing Hamming distance in terms of XOR and Hamming weight
16:38 – Benchmarking Hamming weight functions
18:40 – Starting the solution script, copying in Hamming distance and weight functions
20:30 – Moving on to guessing key size
21:10 – Explaining why this method for guessing key size works (Kasiski examination)
22:35 – “That’s enough talking, let’s get to actually writing this thing”
24:35 – Cutting a corner by generating transpositions of blocks using slice syntax
26:00 – Cracking each transposed cipher and combining the results
27:00 – Writing the main block
29:30 – Testing the solution

Further reading:
https://en.wikipedia.org/wiki/Hamming_weight
https://en.wikipedia.org/wiki/Hamming_distance
https://en.wikipedia.org/wiki/Hacker%27s_Delight
https://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher
https://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher#Kasiski_examination

Set 1, Challenge 7: AES in ECB mode

Challenge link: https://cryptopals.com/sets/1/challenges/7

00:00 – Intro
01:10 – Discussing options: “Cryptography” library
01:35 – Discussing options: Pure Python implementation
02:05 – Discussing options: CryptoHack’s AES challenges
02:35 – Discussing options: “pycrypto” library
03:08 – Discussing options: “pycryptodome” library
03:33 – Creating a requirements.txt file and a virtual environment
04:20 – Starting the solution script
04:58 – Starting the main block
05:28 – Why don’t we always use AES-ECB?
07:05 – Testing the solution

Further reading:
https://en.wikipedia.org/wiki/Block_cipher_mode_of_operation#Electronic_codebook_(ECB)
https://github.com/pycrypto/pycrypto/issues/176
https://www.pycryptodome.org/en/latest/
https://www.pycryptodome.org/en/latest/src/cipher/aes.html
https://cryptohack.org/

Set 1, Challenge 8: Detect AES in ECB mode

Challenge link: https://cryptopals.com/sets/1/challenges/8

00:00 – Intro
00:30 – Describing the solution strategy, showing the famous penguin picture
01:50 – Implementing bytes_to_chunks
04:35 – Starting the main block
05:50 – Deduplicating blocks by converting to set
07:38 – Testing the solution

Further reading:
https://en.wikipedia.org/wiki/Block_cipher_mode_of_operation#Electronic_codebook_(ECB)
https://en.wikipedia.org/wiki/Distinguishing_attack

Thank you!

Before wrapping up this post, I’d like to take a moment to thank Parnian Alimi and Marie-Sarah Lacharite for their valuable feedback and detailed code review.

I would also like to thank the authors of the Cryptopals challenges. I’ve never met any of them, but I’ve spent a lot of time with their work and I appreciate the effort they’ve put into it.

Technical Advisory – SonicWall SMA 100 Series – Unauthenticated Arbitrary File Deletion

10 December 2021 at 02:27
Vendor: SonicWall
Vendor URL: https://www.sonicwall.com/
Versions affected: 10.2.0.8-37sv, 10.2.1.1-19sv
Systems Affected: SMA 100 Series (SMA 200, 210, 400, 410, 500v)
Author: Richard Warren <richard.warren[at]nccgroup[dot]trust>
Risk: CVSS 9.1 (Critical)

Summary

SonicWall SMA 100 Series appliances running firmware versions 10.2.0.8-37sv, 10.2.1.1-19sv and below suffer from an unauthenticated arbitrary file-delete vulnerability which can be exploited by a remote attacker to delete arbitrary files from the underlying Operating System.

This vulnerability exists in the sonicfiles RAC_DOWNLOAD_TAR method, which allows users to download a tar file from a specified SMB share, and is exposed to unauthenticated users. Before a file is downloaded, unlink is called on the local file-path. By exploiting a directory traversal vulnerability in this method, an attacker can cause unlink to be called on a path of their choosing, resulting in arbitrary file deletion.

Note that because the server runs as the nobody user, only files that are owned by nobody can be deleted.

By deleting the persist.db file, an attacker can corrupt the locally stored user database. Depending on the device, deleting this file will either cause the device to immediately reboot, or for the HTTP server to become unresponsive. In either case, when the device is rebooted, the default password of admin:password will be restored – allowing the attacker to log into the administrative interface with known credentials.

Once access to the administrative interface has been gained, the attacker could exploit CVE-2021-20044 to achieve Remote Code Execution as root.

Impact

A remote unauthenticated attacker could exploit this issue to delete arbitrary files from the underlying Operating System. This may include database and configuration files owned by the nobody user. Depending on which file is deleted this could either result in Denial of Service, or takeover of the device.

Recommendation

Upgrade to SMA version 10.2.0.9-41sv, 10.2.1.3-27sv or above.

Vendor Communication

2021-10-29 - Vulnerability reported to SonicWall PSIRT.
2021-11-02 - Reply from SonicWall PSIRT stating that the vulnerability has been "rejected" as a duplicate/known issue.
2021-11-06 - SonicWall confirm they have addressed the other reported issues and shared a draft KB article, which omits details of arbitrary file-deletion and XSS issues. NCC Group requests clarification around this - providing evidence of successful exploitation on latest firmware versions (10.2.0.8-37v and 10.2.1.2-24sv).
2021-11-06 - SonicWall PSIRT respond stating that these issues were rejected, referencing the original triage response.
2021-12-07 - Patch released and SonicWall publish KB article.
2021-12-07 - NCC Group request further clarification on missing vulnerabilities.
2021-12-08 - NCC Group confirms that the arbitrary file-deletion vulnerability was fixed on firmware versions 10.2.0.9-41sv and 10.2.1.3-27sv.
2021-12-08 - SonicWall PSIRT responds stating that the issue was found by internal review prior to NCC Group's report, and for this reason was omitted from the advisory notes.
2021-12-09 - NCC Group advisory released.

Thanks to

Jennifer Fernick and Aaron Haymore from NCC Group for their assistance with disclosure.

About NCC Group

NCC Group is a global expert in cybersecurity and risk mitigation, working with businesses to protect their brand, value and reputation against the ever-evolving threat landscape. With our knowledge, experience and global footprint, we are best placed to help businesses identify, assess, mitigate & respond to the risks they face. We are passionate about making the Internet safer and revolutionizing the way in which organizations think about cybersecurity.

Published Date: 2021-12-09

Written By: Richard Warren

Technical Advisory – SonicWall SMA 100 Series – Unauthenticated Stored XSS

10 December 2021 at 02:22
Vendor: SonicWall
Vendor URL: https://www.sonicwall.com/
Versions affected: 10.2.0.8-37sv, 10.2.1.1-19sv
Systems Affected: SMA 100 Series (SMA 200, 210, 400, 410, 500v)
Author: Richard Warren <richard.warren[at]nccgroup[dot]trust>
Risk: CVSS 8.2 (High)

Summary

SonicWall SMA 100 Series appliances running firmware versions 10.2.0.8-37sv, 10.2.1.1-19sv and below suffer from a Stored Cross-Site Scripting (XSS) vulnerability within the management interface. This vulnerability arises due to lack of sufficient output encoding when displaying postscript file names within the management interface.

Due to CVE-2021-20040, this issue can be exploited by a remote, unauthenticated attacker.

Impact

When combined with CVE-2021-20040, this vulnerability may allow a remote unauthenticated attacker to inject arbitrary JavaScript into a page within the management interface. When an authenticated administrator visits this page within the management interface, arbitrary JavaScript could be executed within the administrators authenticated session.

An attacker with access to an administrative session could later exploit CVE-2021-20044 to achieve Remote Code Execution.

Recommendation

Upgrade to SMA version 10.2.0.9-41sv, 10.2.1.3-27sv or above.

Vendor Communication

2021-10-29 - Vulnerability reported to SonicWall PSIRT.
2021-11-02 - Reply from SonicWall PSIRT stating that the vulnerability has been "rejected" as a duplicate/known issue.
2021-11-06 - SonicWall confirm they have addressed the other reported issues and shared a draft KB article, which omits details of arbitrary file-deletion and XSS issues. NCC Group requests clarification around this - providing evidence of successful exploitation on latest firmware versions (10.2.0.8-37v and 10.2.1.2-24sv).
2021-11-06 - SonicWall PSIRT respond stating that these issues were rejected, referencing the original triage response.
2021-12-07 - Patch released and SonicWall publish KB article.
2021-12-07 - NCC Group request further clarification on missing vulnerabilities.
2021-12-08 - NCC Group confirms that the arbitrary file-deletion vulnerability was fixed on firmware versions 10.2.0.9-41sv and 10.2.1.3-27sv.
2021-12-08 - SonicWall PSIRT responds stating that the issue was found by internal review prior to NCC Group's report, and for this reason was omitted from the advisory notes.
2021-12-09 - NCC Group advisory released.

Thanks to

Jennifer Fernick and Aaron Haymore from NCC Group for their assistance with disclosure.

About NCC Group

NCC Group is a global expert in cybersecurity and risk mitigation, working with businesses to protect their brand, value and reputation against the ever-evolving threat landscape. With our knowledge, experience and global footprint, we are best placed to help businesses identify, assess, mitigate & respond to the risks they face. We are passionate about making the Internet safer and revolutionizing the way in which organizations think about cybersecurity.

Published Date: 2021-12-09

Written By: Richard Warren

Technical Advisory – SonicWall SMA 100 Series – Multiple Unauthenticated Heap-based and Stack-based Buffer Overflow (CVE-2021-20045)

9 December 2021 at 23:33
Vendor: SonicWall
Vendor URL: https://www.sonicwall.com/
Versions affected: 10.2.0.8-37sv, 10.2.1.1-19sv
Systems Affected: SMA 100 Series (SMA 200, 210, 400, 410, 500v)
Author: Richard Warren <richard.warren[at]nccgroup[dot]trust>
Advisory URL: https://psirt.global.sonicwall.com/vuln-detail/SNWLID-2021-0026
CVE Identifier: CVE-2021-20045
Risk: CVSS 9.4 (Critical)

Summary

SonicWall SMA 100 Series appliances running firmware versions 10.2.0.8-37sv, 10.2.1.1-19sv and below, are vulnerable to multiple stack-based and heap-based buffer overflows in the fileexplorer component, which can be reached by an unauthenticated attacker, calling the sonicfiles RAC_COPY_TO method. These vulnerabilities arise due to the unchecked use of strcpy with a fixed size buffer.

Impact

An unauthenticated remote attacker that successfully exploits any of these issues may be able to execute arbitrary code with the privileges of the nobody user.

Vendor Communication

2021-10-29 - Vulnerability reported to SonicWall PSIRT.
2021-11-02 - Acknowledgement from SonicWall PSIRT.
2021-12-01 - SonicWall request that NCC Group withhold technical details until 2022-01-11, releasing high-level advisories on 2021-12-09.
2021-12-03 - NCC Group agrees to suggested disclosure timeline.
2021-12-07 - Patch released and SonicWall publish KB article and security advisory.
2021-12-09 - NCC Group advisory released.

Thanks to

Jennifer Fernick and Aaron Haymore from NCC Group for their assistance with disclosure.

About NCC Group

NCC Group is a global expert in cybersecurity and risk mitigation, working with businesses to protect their brand, value and reputation against the ever-evolving threat landscape. With our knowledge, experience and global footprint, we are best placed to help businesses identify, assess, mitigate & respond to the risks they face. We are passionate about making the Internet safer and revolutionizing the way in which organizations think about cybersecurity.

Published Date: 2021-12-09

Written By: Richard Warren

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