This blog post is part of a series and a re-posting of the original article “Fuzzing 101” that I have written for Yarix on YLabs. Introduction In this article, I would like to introduce fuzz testing as part of a vast overview of software testing approaches used to discover bugs and vulnerabilities within applications, protocols, […]

The post Software Testing Methodologies & Approaches to Fuzzing appeared first on VoidSec.

Executive Summary

Babuk ransomware is a new ransomware threat discovered in 2021 that has impacted at least five big enterprises, with one already paying the criminals $85,000 after negotiations. As with other variants, this ransomware is deployed in the network of enterprises that the criminals carefully target and compromise. Using MVISION Insights, McAfee was able to plot the telemetry of targets, revealing that the group is currently targeting the transportation, healthcare, plastic, electronics, and agricultural sectors across multiple geographies.

Figure 1. Infection map (source: MVISION Insights)

Coverage and Protection Advice

McAfee’s EPP solution covers Babuk ransomware with an array of prevention and detection techniques.

ENS ATP provides behavioral content focusing on proactively detecting the threat while also delivering known IoCs for both online and offline detections. For DAT based detections, the family will be reported as Ransom-Babuk!<hash>. ENS ATP adds 2 additional layers of protection thanks to JTI rules that provide attack surface reduction for generic ransomware behaviors and RealProtect (static and dynamic) with ML models targeting ransomware threats.

Updates on indicators are pushed through GTI, and customers of Insights will find a threat-profile on this ransomware family that is updated when new and relevant information becomes available.

Initially, in our research the entry vector and the complete tactics, techniques and procedures (TTPs) used by the criminals behind Babuk remained unclear.

However, when its affiliate recruitment advertisement came online, and given the specific underground meeting place where Babuk posts, defenders can expect similar TTPs with Babuk as with other Ransomware-as-a-Service families.

In its recruitment posting Babuk specifically asks for individuals with pentest skills, so defenders should be on the lookout for traces and behaviors that correlate to open source penetration testing tools like winPEAS, Bloodhound and SharpHound, or hacking frameworks such as CobaltStrike, Metasploit, Empire or Covenant. Also be on the lookout for abnormal behavior of non-malicious tools that have a dual use, such as those that can be used for things like enumeration and execution, (e.g., ADfind, PSExec, PowerShell, etc.) We advise everyone to read our blogs on evidence indicators for a targeted ransomware attack (Part1, Part2).

Looking at other similar Ransomware-as-a-Service families we have seen that certain entry vectors are quite common amongst ransomware criminals:

• E-mail Spearphishing (T1566.001). Often used to directly engage and/or gain an initial foothold, the initial phishing email can also be linked to a different malware strain, which acts as a loader and entry point for the ransomware gangs to continue completely compromising a victim’s network. We have observed this in the past with Trickbot and Ryuk, Emotet and Prolock, etc.
• Exploit Public-Facing Application (T1190) is another common entry vector; cyber criminals are avid consumers of security news and are always on the lookout for a good exploit. We therefore encourage organizations to be fast and diligent when it comes to applying patches. There are numerous examples in the past where vulnerabilities concerning remote access software, webservers, network edge equipment and firewalls have been used as an entry point.
• Using valid accounts (T1078) is and has been a proven method for cybercriminals to gain a foothold. After all, why break the door if you have the keys? Weakly protected Remote Desktop Protocol (RDP) access is a prime example of this entry method. For the best tips on RDP security, we would like to highlight our blog explaining RDP security.
• Valid accounts can also be obtained via commodity malware such as infostealers, that are designed to steal credentials from a victim’s computer. Infostealer logs containing thousands of credentials are purchased by ransomware criminals to search for VPN and corporate logins. As an organization, robust credential management and multi-factor authentication on user accounts is an absolute must have.

When it comes to the actual ransomware binary, we strongly advise updating and upgrading your endpoint protection, as well as enabling options like tamper protection and rollback. Please read our blog on how to best configure ENS 10.7 to protect against ransomware for more details.

Summary of the Threat

• Babuk ransomware is a new ransomware family originally detected at the beginning of 2021.
• Its operators adopted the same operating methods as other ransomware families and leaked the stolen data on a public website: hxxp://gtmx56k4hutn3ikv.onion/.
• Babuk’s codebase and artefacts are highly similar to Vasa Locker’s.
• Babuk advertises on both English-speaking and Russian-speaking forums, where it seems the former is used for announcements and the latter is focused on affiliate recruitment and ransomware updates.
• The individuals behind Babuk ransomware have explicitly expressed themselves negatively against the BlackLivesMatter (BLM) and LGBT communities.
• At least 5 companies have been breached as of January 15, 2021.
• The ransomware supports command line operation and embeds three different built-in commands used to spread itself and encrypt network resources.
• It checks the services and processes running so it can kill a predefined list and avoid detection.
• There are no local language checks, in contrast to other ransomware gangs that normally spare devices in certain countries.
• The most recent variant has been spotted packed.

Learn more about the inner working of Babuk, underground forum activity, Yara Rules, Indicators of Compromise & Mitre ATT&CK techniques used by reading our detailed technical analysis.

Babuk Ransomware

Learn more about the inner working of Babuk, underground forum activity, Yara Rules, Indicators of Compromise & Mitre ATT&CK techniques used.
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The post Babuk Ransomware appeared first on McAfee Blog.

On February 17^th, 2021, McAfee disclosed findings based on a 10-month long disclosure process with major video conferencing vendor Agora, Inc.  As we disclosed the findings to Agora in April 2020, this lengthy disclosure timeline represents a nonstandard process for McAfee but was a joint agreement with the vendor to allow sufficient time for the development and release of a secure SDK. The release of the SDK mitigating the vulnerability took place on December 17^th, 2020. Given the implications of snooping and spying on video and audio calls, we felt it was important to provide Agora the extended disclosure time. The affected users of Agora include popular voice and video messaging apps, with one notable application being the popular new iOS app known as Clubhouse.

Clubhouse Application Screenshot

Clubhouse has made headlines recently as one of the newest players in the social networking sphere, rising in popularity after a series of high-profile users including Elon Musk, Kanye West and influencers in various geographies posted about the platform. Released in April of 2020, Clubhouse quickly carved out a niche in Chinese social media as the platform to discuss sensitive social and political topics – perhaps aided by its invite-only approach to membership – and the spotlight shined on it by these key players further propelled it into viral status early this year. Perhaps unsurprisingly, the application was blocked for use in China on February 8^th, 2021.

Last week, Stanford Internet Observatory (SIO) released research regarding the popular Clubhouse app’s use of Agora real-time engagement software and suggested that Agora could have provided the Chinese government access to Clubhouse user information and communications.  While the details of Stanford’s disclosure focus on the audio SDK compared to our work on the video SDK, the functionality and flaw are similar to our recent disclosure, CVE-2020-25605.  This includes the plaintext transmission of app ID, channel ID and token – credentials necessary to join either audio or video calls. We can confirm that Clubhouse updated to the most recent version of the Agora SDK on February 16^th – just a day prior to our public disclosure.

Despite the recent noise surrounding Clubhouse, the reality is that this application is just one of many applications that leverage the Agora SDK. Among others, we investigated the social apps eHarmony, Skout, and MeetMe, along with several widely-used healthcare apps, some of which have a significantly larger user base. For example, MeetGroup (comprised of several apps) reported approximately 18 million monthly users compared to Clubhouse, which had approximately 600k total users as of December 2020.

We felt it was important to highlight these data points and are continuing to investigate these applications as well as monitor any potential instances of malicious actors exploiting this vulnerability. Given that Agora has released an updated SDK that fixes the call setup issues, vulnerable applications should have already switched to the secure calling SDK, thus protecting the sensitive audio and video call data as many claim to do. With that in mind, we decided to check back in with some of the Agora-based apps we previously investigated to confirm whether they had updated to the patched version. We were surprised to see many, as of February 18, 2020, still had not:

With the context around censorship and basic privacy concerns, it will be interesting to see if these and many other apps using the vulnerable SDK update quickly, or even ever, and what kind of lasting effects these types of findings have on users’ trust and confidence in social media platforms.

For more on McAfee ATR’s research into the Agora SDK, please see our technical research blog.

For information on how users can protect themselves when using such apps, please see our consumer safety tips blog.

The post Beyond Clubhouse: Vulnerable Agora SDKs Still in Widespread Use appeared first on McAfee Blog.

The McAfee Advanced Threat Research (ATR) team is committed to uncovering security issues in both software and hardware to help developers provide safer products for businesses and consumers. We recently investigated and published several findings on a personal robot called “temi”, which can be read about in detail here. A byproduct of our robotic research was a deeper dive into a video calling software development kit (SDK) created by Agora.io. Agora’s SDKs are used for voice and video communication in applications across multiple platforms. Several of the most popular mobile applications utilizing the vulnerable SDK included social apps such as eHarmony, Plenty of Fish, MeetMe and Skout, and healthcare apps such as Talkspace, Practo and Dr. First’s Backline. In early 2020, our research into the Agora Video SDK led to the discovery of sensitive information sent unencrypted over the network. This flaw, CVE-2020-25605, may have allowed an attacker to spy on ongoing private video and audio calls. At the time of writing, McAfee is unaware of any instances of this vulnerability being exploited in the wild. We reported this research to Agora.io on April 20, 2020 and the company, as of December 17th, 2020 released a new SDK, version 3.2.1, which mitigated the vulnerability and eliminated the corresponding threat to users.

Encryption has increasingly become the new standard for communication; often even in cases where data privacy is not explicitly sensitive. For example, all modern browsers have begun to migrate to newer standards (HTTP/2) which enforce encryption by default, a complete change from just a few years ago where a significant amount of browsing traffic was sent in clear text and could be viewed by any interested party. While the need to protect truly sensitive information such as financial data, health records, and other personally identifiable information (PII) has long been standardized, consumers are increasingly expecting privacy and encryption for all web traffic and applications. Furthermore, when encryption is an option provided by a vendor, it must be easy for developers to implement, adequately protect all session information including setup and teardown, and still meet the developers’ many use cases. These core concepts are what led us to the findings discussed in this blog.

To boldly go where no one has gone before

As part of our analysis of the temi ecosystem, the team reviewed the Android application that pairs with the temi robot. During this analysis, a hardcoded key was discovered in the app.

This raised the question: What is this key and what is it used for? Thanks to the detailed logging provided by the developers, we had a place to start, https://dashboard.agora.io.

What is Agora?

According to the website – “Agora provides the SDKs and building blocks to enable a wide range of real-time engagement possibilities” In the context of our initial robot project, it simply provides the technology required to make audio and video calls. In a broader context, Agora is used for a variety of applications including social, retail, gaming and education, among others.

Agora allows anyone to create an account and download its SDKs for testing from its website, which also provides extensive documentation. Its GitHub repositories also provide detailed sample projects on how to use the product. This is amazing for developers, but also very useful to security researchers and hackers. Using the logging comments from the above code we can look at the documentation to understand what an App ID is and what it is used for.

The last two sentences of this documentation really grabbed our attention. We had found a key which is hardcoded into an Android application that “anyone can use on any Agora SDK” and is “prudent to safeguard”.

Agora provides several different SDKs with different functionality. Since we encountered Agora through use of the video SDK, we decided to focus on only this SDK for the rest of this research. Simulating the mindset of an attacker, we began to investigate what this App ID or key could be used for. Furthermore, in the context of the video SDK, the question evolved into whether an attacker could interact with this video and audio traffic.

“They’ve done studies, you know. 60% of the time, encrypting works every time.”

Since Agora provides sample projects and allows for free developer accounts, the best way to understand what potential attack vectors exists is to use these tools. Examining the GitHub example projects and the following associated documentation, we can learn exactly what is needed and how a normal user is connected to a video call.

Here we see in the example code the App ID being used to create a new “RtcEngine” object. As can be seen in the documentation, creating an RtcEngine is the foundation and first step needed to create any video call.

If we continue to examine the example code and look at the documentation’s steps for connecting to a call, we come to a function named “joinChannel”.

This function is responsible for connecting an end user to a call. In the example code, there are four parameters, three of which are hardcoded and one of which can be set to null. Without doing too much more digging, it appears that while Agora lists the App ID as important, it is not the only component needed to join a video call. An attacker would also require the values passed to the joinChannel API in order to join a call. If we assume that these values are only hardcoded for the purpose of a demo application, how would an attacker obtain the other necessary values? Code is an awesome resource, but when it comes to a network conversation the truth is in the packets. By running this example code and capturing traffic with Wireshark, we can further our understanding of how this system works.

When looking at the traffic, what immediately stands out is the values passed to joinChannel in the example code above. They are sent in plaintext across the network, in addition to the App ID needed to initiate the RtcEngine. Considering this is an example app, it is important to understand the difference between a test scenario and a production scenario. As noted in the code in Figure 5, the “token” parameter is being set to null. What is a token in this context, and would that affect the security of these parameters? We can use the Agora documentation to understand that a token is designed to be randomly generated and provide more security for a session.

With tokens being an option, it is important we see what the traffic looks like in a scenario where the token parameter is not null. The use of a token is hopefully what we find in production or at least is what is recommended in production by Agora.

Running the example application again, this time using a token and capturing traffic, we can discover that the token is also a non-issue for an attacker.

The token is sent in plaintext just like the other parameters! You may have noticed this capture does not show the App ID; in this case the App ID is still sent in plaintext, in a different packet.

The Spy Who Loved Me

Information being sent in plaintext across the network to initiate a video call is one thing, but can this actually be used by an attacker to spy on a user? Will the call support a third party? Will the user be notified of a new connection? In order to answer these questions, we can use the example applications provided by Agora to run some tests. The diagram below shows the scenario we are going to try and create for testing.

The first step for an attacker sitting on the network is to be able to identify the proper network traffic containing the sensitive information. With this information from the network packets, they can then attempt to join the call that is in progress. Using a Python framework called Scapy, we built a network layer in less than 50 lines of code to help easily identify the traffic the attacker cares about. This was done by reviewing the video call traffic and reverse engineering the protocol. Like a lot of reversing, this is done by using context clues and a lot of “guess and check”. Strings help us identify the use for certain fields and provide clues to what the fields around them might be. In some cases, fields are still unknown; however, this is normal. An attacker or researcher only needs to decipher enough of a packet to make an attack possible.

The above code identifies, parses, and displays in human readable form, only the important information in the three main packet types discovered in the Agora traffic. From here we can automate the scenario outlined in Figure 10 using Python with a few modifications to the example apps. It totally works! The demo video below shows an attacker sniffing network traffic to gather the call information and then launching their own Agora video application to join the call, completely unnoticed by normal users.

Besides using a token, were there any other security measures available to developers that might have mitigated the impact of this vulnerability? Per Agora’s documentation, the developer has the option to encrypt a video call. We also tested this, and the App ID, Channel Name, and Token are still sent in plaintext when the call is encrypted. An attacker can still get these values; however, they cannot view the video or hear the audio of the call. Despite this, the attacker can still utilize the App ID to host their own calls at the cost of the app developer. We will discuss in the next section why, even though encryption is available, it is not widely adopted, making this mitigation largely impractical.

You talkin’ to me? Are you talkin’ to me?

This research started with the discovery of an App ID hardcoded into “temi”, the personal robot we were researching. Agora documents clearly on its website that this App ID should be “kept safe”, which we discovered as a vulnerability while researching temi. However, further examination shows that even if temi had kept this value safe, it is sent in cleartext over the network along with all the other values needed to join the call. So how does this impact other consumers of the Agora SDK outside of temi?

Agora’s website claims – “Agora’s interactive voice, video, and messaging SDKs are embedded into mobile, web and desktop applications across more than 1.7 billion devices globally.” To ensure our comparisons would be reasonable, we looked only at other Android applications which used the video SDK. We also focused on apps with a very large install base.

To give a quick cross-section of apps on Google Play that utilize Agora, let’s focus on MeetMe, Skout, Nimo TV, and, of course, temi. Google Play reports the number of installs at 50 million+ each for MeetMe and Skout, 10 million+ for Nimo TV and 1000+ for temi. By examining the applications’ decompiled code, we can easily determine all four of these applications have hardcoded App IDs and do not enable encryption. We see very similar code as below in all the applications:

The most important line here is the call to setEncryptionSecret which is being passed the “null” parameter. We can reference the Agora documentation to understand more about this call.

Even though the encryption functions are being called, the application developers are actually disabling the encryption based on this documentation. A developer paying close attention might ask if this is the only place the API is being called. Could it be set elsewhere? The team searched the decompiled code for all the applications reviewed and was unable to find any other instance of the API call, leading us to believe this is the only call being made. With this in mind, the cleartext traffic has a greater impact that goes well beyond a personal robot application to millions – potentially billions – of users. Without encryption enabled and the setup information passed in cleartext, an attacker can spy on a very large range of users.

Although Agora should not have been sending this sensitive information unencrypted, if it offers an encrypted traffic option, why is it not being used? This would at least protect the video and audio of the call, even though an attacker is able to join. While it is impossible to be certain, one reason might be because the Agora encryption options require a pre-shared key, which can be seen in its example applications posted on GitHub. The Agora SDK itself did not provide any secure way to generate or communicate the pre-shared key needed for the phone call, and therefore this was left up to the developers. Many calling models used in applications want to give the user the ability to call anyone without prior contact. This is difficult to implement into a video SDK post-release since a built-in mechanism for key sharing was not included. It is also worth noting that, generally, the speed and quality of a video call is harder to maintain while using encryption. These may be a few of the reasons why these application developers have chosen to not use the encryption for the video and audio. Now that the SDK properly and transparently handles encryption of call setup, developers have the opportunity to leverage a more secure method of communication for traffic. Additionally, developers still have the option to add full encryption to further protect video and audio streams.

Agora has published a list of best practices for all its developers and partners here, which does include use of encryption when possible. We generally recommend following vendors’ security best practices as they are designed to apply to the product or service directly.  In this case, the vulnerability would still exist; however, its effectiveness would be extremely limited if the published best practices were followed. Although we have found Agora’s recommendations were largely not being adopted, Agora has been actively communicating with customers throughout the vulnerability disclosure process to ensure its recommended processes and procedures are widely implemented.

Yo, Adrian, we did it. We did it.

Privacy is always a top concern for consumers, but also remains an enticing threat vector for attackers. If we look at the two biggest apps we investigated (MeetMe and Skout), both are rated for mature audiences (17+) to “meet new people” and both advertise over 100 million users. MeetMe also mentions “flirting” on the Google Play store and its website has testimonies about people meeting the “love of their life”. Although they are not explicitly advertised as dating apps, it would be reasonable to draw the conclusion that it is at least one of their functions. In the world of online dating, a breach of security or the ability to spy on calls could lead to blackmail or harassment by an attacker. Other Agora developer applications with smaller customer bases, such as the temi robot, are used in numerous industries such as hospitals, where the ability to spy on conversations could lead to the leak of sensitive medical information.

One goal of the McAfee Advanced Threat Research team is to identify and illuminate a broad spectrum of threats in today’s complex and constantly evolving landscape. As per McAfee’s responsible disclosure policy, McAfee ATR informed Agora as soon as the vulnerability was confirmed to be exploitable. Agora was very receptive and responsive to receiving this information and further advanced its current security capabilities by providing developers with an SDK option (version 3.2.1) to encrypt the initial call setup information, mitigating this vulnerability.  We have tested this new SDK and can confirm it fully mitigates CVE-2020-25605. At the time of writing, McAfee is unaware of any instances of this vulnerability being exploited in the wild, which demonstrates another powerful success story of mitigating an issue which may have affected millions of users before it is used for malicious purposes. Our partnership with Agora resulted in the release of a more secure SDK which has empowered developers across multiple companies to produce more secure video calling applications. We strongly recommend any development team which uses the Agora SDK to upgrade to the latest version, follow Agora’s outlined best practices, and implement full encryption wherever possible.

Vulnerability Details

CVE: CVE-2020-25605
CVSSv3 Rating: 7.5/6.7
CVSS String: AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N/E:P/RL:O/RC:C
CVE Description: Cleartext transmission of sensitive information in Agora Video SDK prior to 3.1 allows a remote attacker to obtain access to audio and video of any ongoing Agora video call through observation of cleartext network traffic.

The post Don’t Call Us We’ll Call You: McAfee ATR Finds Vulnerability in Agora Video SDK appeared first on McAfee Blog.

In fact, looking back at just the last 8 months of Microsoft patches, we’ve tracked at least 25 significant vulnerabilities directly related to the Windows network stack. These have ranged from DNS to NTFS, SMB to NFS and recently, several tcp/ip bugs in fragmentation and packet reassembly for IPv4 and IPv6.

That brings us to this month’s patch Tuesday, which contains several more high-profile critical vulnerabilities in tcpip.sys. We’ll focus on three of these, including 2 marked as “remote code execution” bugs, which could lead to wormability if code execution is truly possible, and a persistent denial-of-service vulnerability which could cause a permanent Blue Screen of Death on the latest versions of Windows.

There are clear similarities between all 3, indicating both Microsoft and researchers external to Microsoft are highly interested in auditing this library, and are having success in weeding out a number of interesting bugs. The following is a short summary of each bug and the progress we have made to date in analyzing them.

What is CVE-2021-24086?
The first vulnerability analyzed in this set is a Denial-of-Service (DOS) attack. Generally, these types of bugs are rather uninteresting; however, a few can have enough of an impact that although an attacker can’t get code execution, they are well worth discussing. This is one of those few. One of the things that boost this vulnerability’s impact is the fact it is internet routable and many devices using IPv6 can be directly accessible over the internet, even when behind a router. It is also worth noting that the default Windows Defender Firewall configuration does not mitigate this attack. In a worst-case scenario, an attacker could spray this attack and put it on a continuous loop to potentially cause a “permanent” or persistent DOS to a wide range of systems.

This vulnerability exists when Windows’ tcpip.sys driver attempts to reassemble fragmented IPv6 packets. As a result, this attack requires many packets to be successful.  The root cause of this vulnerability is a NULL pointer dereference which occurs in Ipv6pReassembleDatagram. The crash occurs when reassembling a packet with around 0xffff bytes of extension headers.  It should be impossible to send a packet with that many bytes in the extension headers according to the RFC, however this is not considered in the Ipv6pReceiveFragments function when calculating the unfragmented length. Leveraging a proof-of-concept through the Microsoft MAPP program, McAfee was easily able to reproduce this bug, demonstrating it has the potential to be seen in the wild.

What is CVE-2021-24094?
This vulnerability is classified by Remote Code Execution (RCE), though our analysis thus far, code execution comes with unique challenges. Similar to CVE-2021-24086, this issue involves IPv6 packet reassembly by the Windows tcpip.sys driver. It is different from 24086 in that it doesn’t require a large packet with extension headers, and it is not a NULL pointer dereference. Instead, it is a dangling pointer which, if the right packet data is sprayed from an attacker over IPv6, will causes the pointer to be overwritten and reference an arbitrary memory location. When the data at the pointer’s target is accessed, it causes a page fault and thus a Blue Screen of Death (BSOD). Additionally, an attacker can create persistence of the DoS by continually pointing the attack at a victim machine.

While the reproduction of this issue causes crashes on the target in all reproductions so far, it’s unclear how easy it would be to force the pointer to a location that would cause valid execution without crashing. The pointer would need to point to a paged-in memory location that had already been written with additional data that could manipulate the IPv6 reassembly code, which would likely not come from this attack alone, but may require a separate attack to do so.

What is CVE-2021-24074?
CVE-2021-24074 is a potential RCE in tcpip.sys triggered during the reassembly of fragmented IPv4 packets in conjunction with a confusion of IPv4 IP Options. During reassembly, it is possible to have two fragments with different IP Options; this is against the IPv4 standard, but Windows will proceed further, while failing to perform proper sanity checks for the respective options. This type confusion can be leveraged to trigger an Out of Bounds (OOB) read and write by “smuggling” an IP option Loose source and record route (LSRR) with invalid parameters. This option is normally meant to specify the route a packet should take. It has a variable length, starts with a pointer field, and is followed by a list of IP addresses to route the packet through.

By leveraging the type confusion, we have an invalid pointer field that will point beyond the end of the routing list provided in the packet. When the routing function Ipv4pReceiveRoutingHeader looks up the next hop for the packet, it will OOB read this address (as a DWORD) and perform a handful of checks on it. If these checks are successful, the IP stack will attempt to route the packet to its next hop by copying the original packet and then writing its own IP address in the record route. Because this write relies on the same invalid pointer value as before, this turns out to be an OOB write (beyond the end of the newly allocated packet). The content of this OOB write is the IP address of the target machine represented as a DWORD (thus, not controlled by the attacker).

Microsoft rates this bug as “Exploitation more likely”, however exploitation might not be as straightforward as it sounds. For an attack to succeed, one would have to groom the kernel heap to place a certain value to be read during the OOB read, and then make it so the OOB write would corrupt something of interest. Likely, a better exploitation primitive would need to be established first in order to successfully exploit the bug. For instance, leveraging the OOB write to corrupt another data structure that could lead to information disclosure and/or a write-what-where scenario.

From a detection standpoint, the telltale signs of an active exploitation would be fragmented packets whose IP Options vary between fragments. For instance, the first fragment would not contain an LSRR option, while the second fragment would. This would likely be accompanied by a heavy traffic meant to shape the kernel heap.

Similarities and Impact Assessment
There are obvious similarities between all three of these vulnerabilities. Each is present in the tcpip.sys library, responsible for parsing IPv4 and IPv6 traffic. Furthermore, the bugs all deal with packet reassembly and the RCE vulnerabilities leverage similar functions for IPv4 and IPv6 respectively. Given a combination of public and Microsoft-internal attribution, it’s clear that researchers and vendor alike are chasing down the same types of bugs. Whenever we see vulnerabilities in network stacks or Internet-routed protocols, we’re especially interested to determine difficulty of exploitation, wormability, and impact. For vulnerabilities such as the RCEs above, a deep dive is essential to understand the likelihood of these types of flaws being built into exploit kits or used in targeted attacks and are prime candidates for threat actors to weaponize. Despite the real potential for harm, the criticality of these bugs is somewhat lessened by a higher bar to exploitation and the upcoming patches from Microsoft. We do expect to see additional vulnerabilities in the TCP/IP stack and will continue to provide similar analysis and guidance. As it is likely to take some time for threat actors to integrate these vulnerabilities in a meaningful way, the best course of action, as always, is widespread mitigation via patching.

The post Researchers Follow the Breadcrumbs: The Latest Vulnerabilities in Windows’ Network Stack appeared first on McAfee Blog.

A Year in Review of 0-days Exploited In-The-Wild in 2020

Posted by Maddie Stone, Project Zero

2020 was a year full of 0-day exploits. Many of the Internet’s most popular browsers had their moment in the spotlight. Memory corruption is still the name of the game and how the vast majority of detected 0-days are getting in. While we tried new methods of 0-day detection with modest success, 2020 showed us that there is still a long way to go in detecting these 0-day exploits in-the-wild. But what may be the most notable fact is that 25% of the 0-days detected in 2020 are closely related to previously publicly disclosed vulnerabilities. In other words, 1 out of every 4 detected 0-day exploits could potentially have been avoided if a more thorough investigation and patching effort were explored. Across the industry, incomplete patches — patches that don’t correctly and comprehensively fix the root cause of a vulnerability — allow attackers to use 0-days against users with less effort.

Since mid-2019, Project Zero has dedicated an effort specifically to track, analyze, and learn from 0-days that are actively exploited in-the-wild. For the last 6 years, Project Zero’s mission has been to “make 0-day hard”. From that came the goal of our in-the-wild program: “Learn from 0-days exploited in-the-wild in order to make 0-day hard.” In order to ensure our work is actually making it harder to exploit 0-days, we need to understand how 0-days are actually being used. Continuously pushing forward the public’s understanding of 0-day exploitation is only helpful when it doesn’t diverge from the “private state-of-the-art”, what attackers are doing and are capable of.

Over the last 18 months, we’ve learned a lot about the active exploitation of 0-days and our work has matured and evolved with it. For the 2nd year in a row, we’re publishing a “Year in Review” report of the previous year’s detected 0-day exploits. The goal of this report is not to detail each individual exploit, but instead to analyze the exploits from the year as a group, looking for trends, gaps, lessons learned, successes, etc. If you’re interested in each individual exploit’s analysis, please check out our root cause analyses.

When looking at the 24 0-days detected in-the-wild in 2020, there’s an undeniable conclusion: increasing investment in correct and comprehensive patches is a huge opportunity for our industry to impact attackers using 0-days. 

A correct patch is one that fixes a bug with complete accuracy, meaning the patch no longer allows any exploitation of the vulnerability. A comprehensive patch applies that fix everywhere that it needs to be applied, covering all of the variants. We consider a patch to be complete only when it is both correct and comprehensive. When exploiting a single vulnerability or bug, there are often multiple ways to trigger the vulnerability, or multiple paths to access it. Many times we’re seeing vendors block only the path that is shown in the proof-of-concept or exploit sample, rather than fixing the vulnerability as a whole, which would block all of the paths. Similarly, security researchers are often reporting bugs without following up on how the patch works and exploring related attacks.

While the idea that incomplete patches are making it easier for attackers to exploit 0-days may be uncomfortable, the converse of this conclusion can give us hope. We have a clear path toward making 0-days harder. If more vulnerabilities are patched correctly and comprehensively, it will be harder for attackers to exploit 0-days.

This vulnerability looks familiar 🤔

As stated in the introduction, 2020 included 0-day exploits that are similar to ones we’ve seen before. 6 of 24 0-days exploits detected in-the-wild are closely related to publicly disclosed vulnerabilities. Some of these 0-day exploits only had to change a line or two of code to have a new working 0-day exploit. This section explains how each of these 6 actively exploited 0-days are related to a previously seen vulnerability. We’re taking the time to detail each and show the minimal differences between the vulnerabilities to demonstrate that once you understand one of the vulnerabilities, it’s much easier to then exploit another.

Internet Explorer JScript CVE-2020-0674

CVE-2020-0674 is the fourth vulnerability that’s been exploited in this bug class in 2 years. The other three vulnerabilities are CVE-2018-8653, CVE-2019-1367, and CVE-2019-1429. In the 2019 year-in-review we devoted a section to these vulnerabilities. Google’s Threat Analysis Group attributed all four exploits to the same threat actor. It bears repeating, the same actor exploited similar vulnerabilities four separate times. For all four exploits, the attacker used the same vulnerability type and the same exact exploitation method. Fixing these vulnerabilities comprehensively the first time would have caused attackers to work harder or find new 0-days.

JScript is the legacy Javascript engine in Internet Explorer. While it’s legacy, by default it is still enabled in Internet Explorer 11, which is a built-in feature of Windows 10 computers. The bug class, or type of vulnerability, is that a specific JScript object, a variable (uses the VAR struct), is not tracked by the garbage collector. I’ve included the code to trigger each of the four vulnerabilities below to demonstrate how similar they are. Ivan Fratric from Project Zero wrote all of the included code that triggers the four vulnerabilities.

CVE-2018-8653

In December 2018, it was discovered that CVE-2018-8653 was being actively exploited. In this vulnerability, the this variable is not tracked by the garbage collector in the isPrototypeof callback. McAfee also wrote a write-up going through each step of this exploit.

CVE-2019-1367

In September 2019, CVE-2019-1367 was detected as exploited in-the-wild. This is the same vulnerability type as CVE-2018-8653: a JScript variable object is not tracked by the garbage collector. This time though the variables that are not tracked are in the arguments array in the Array.sort callback.

CVE-2019-1429

The CVE-2019-1367 patch did not actually fix the vulnerability triggered by the proof-of-concept above and exploited in the in-the-wild. The proof-of-concept for CVE-2019-1367 still worked even after the CVE-2019-1367 patch was applied!

In November 2019, Microsoft released another patch to address this gap. CVE-2019-1429 addressed the shortcomings of the CVE-2019-1367 and also fixed a variant. The variant is that the variables in the arguments array are not tracked by the garbage collector in the toJson callback rather than the Array.sort callback. The only difference between the variant triggers is the highlighted lines. Instead of calling the Array.sort callback, we call the toJSON callback.

CVE-2020-0674

In January 2020, CVE-2020-0674 was detected as exploited in-the-wild. The vulnerability is that the named arguments are not tracked by the garbage collector in the Array.sort callback. The only changes required to the trigger for CVE-2019-1367 is to change the references to arguments[] to one of the arguments named in the function definition. For example, we replaced any instances of arguments[0] with arg1.

CVE-2020-0968

Unfortunately CVE-2020-0674 was not the end of this story, even though it was the fourth vulnerability of this type to be exploited in-the-wild. In April 2020, Microsoft patched CVE-2020-0968, another Internet Explorer JScript vulnerability. When the bulletin was first released, it was designated as exploited in-the-wild, but the following day, Microsoft changed this field to say it was not exploited in-the-wild (see the revisions section at the bottom of the advisory).

In addition to the vulnerabilities themselves being very similar, the attacker used the same exploit method for each of the four 0-day exploits. This provided a type of “plug and play” quality to their 0-day development which would have reduced the amount of work required for each new 0-day exploit.

Firefox CVE-2020-6820

Mozilla patched CVE-2020-6820 in Firefox with an out-of-band security update in April 2020. It is a use-after-free in the Cache subsystem.

CVE-2020-6820 is a use-after-free of the CacheStreamControlParent when closing its last open read stream. The read stream is the response returned to the context process from a cache query. If the close or abort command is received while any read streams are still open, it triggers StreamList::CloseAll. If the StreamControl (must be the Parent which lives in the browser process in order to get the use-after-free in the browser process; the Child would only provide in renderer) still has ReadStreams when StreamList::CloseAll is called, then this will cause the CacheStreamControlParent to be freed. The mId member of the CacheStreamControl parent is then subsequently accessed, causing the use-after-free.

The execution patch for CVE-2020-6820 is:

CVE-2020-6820 is a variant of an internally found Mozilla vulnerability, Bug 1507180. 1507180 was discovered in November 2018 and patched in December 2019. 1507180 is a use-after-free of the ReadStream in mReadStreamList in StreamList::CloseAll. While it was patched in December, an explanatory comment for why the December 2019 patch was needed was added in early March 2020.

For 150718 the execution path was the same as for CVE-2020-6820 except that the the use-after-free occurred earlier, in StreamControl::CloseAllReadStreams rather than a few calls “higher” in StreamList::CloseAll. 

In my personal opinion, I have doubts about whether or not this vulnerability was actually exploited in-the-wild. As far as we know, no one (including myself or Mozilla engineers [1, 2]), has found a way to trigger this exploit without shutting down the process. Therefore, exploiting this vulnerability doesn’t seem very practical. However, because it was marked as exploited in-the-wild in the advisory, it remains in our in-the-wild tracking spreadsheet and thus included in this list.

Chrome for Android CVE-2020-6572

CVE-2020-6572 is use-after-free in MediaCodecAudioDecoder::~MediaCodecAudioDecoder(). This is Android-specific code that uses Android's media decoding APIs to support playback of DRM-protected media on Android. The root of this use-after-free is that a `unique_ptr` is assigned to another, going out of scope which means it can be deleted, while at the same time a raw pointer from the originally referenced object isn't updated.  

More specifically, MediaCodecAudioDecoder::Initialize doesn't reset media_crypto_context_ if media_crypto_ has been previously set. This can occur if MediaCodecAudioDecoder::Initialize is called twice, which is explicitly supported. This is problematic when the second initialization uses a different CDM than the first one. Each CDM owns the media_crypto_context_ object, and the CDM itself (cdm_context_ref_) is a `unique_ptr`. Once the new CDM is set, the old CDM loses a reference and may be destructed. However, MediaCodecAudioDecoder still holds a raw pointer to media_crypto_context_ from the old CDM since it wasn't updated, which results in the use-after-free on media_crypto_context_ (for example, in MediaCodecAudioDecoder::~MediaCodecAudioDecoder).

This vulnerability that was exploited in-the-wild was reported in April 2020. 7 months prior, in September 2019, Man Yue Mo of Semmle reported a very similar vulnerability, CVE-2019-13695. CVE-2019-13695 is also a use-after-free on a dangling media_crypto_context_ in MojoAudioDecoderService after releasing the cdm_context_ref_. This vulnerability is essentially the same bug as CVE-2020-6572, it’s just triggered by an error path after initializing MojoAudioDecoderService twice rather than by reinitializing the MediaCodecAudioDecoder.

In addition, in August 2019, Guang Gong of Alpha Team, Qihoo 360 reported another similar vulnerability in the same component. The vulnerability is where the CDM could be registered twice (e.g. MojoCdmService::Initialize could be called twice) leading to use-after-free. When MojoCdmService::Initialize was called twice there would be two map entries in cdm_services_, but only one would be removed upon destruction, and the other was left dangling. This vulnerability is CVE-2019-5870. Guang Gong used this vulnerability as a part of an Android exploit chain. He presented on this exploit chain at Blackhat USA 2020, “TiYunZong: An Exploit Chain to Remotely Root Modern Android Devices”.

While one could argue that the vulnerability from Guang Gong is not a variant of the vulnerability exploited in-the-wild, it was at the very least an early indicator that the Mojo CDM code for Android had life-cycle issues and needed a closer look. This was noted in the issue tracker for CVE-2019-5870 and then brought up again after Man Yue Mo reported CVE-2019-13695.

Windows splwow64 CVE-2020-0986

CVE-2020-0986 is an arbitrary pointer dereference in Windows splwow64. Splwow64 is executed any time a 32-bit application wants to print a document. It runs as a Medium integrity process. Internet Explorer runs as a 32-bit application and a Low integrity process. Internet Explorer can send LPC messages to splwow64. CVE-2020-0986 allows an attacker in the Internet Explorer process to control all three arguments to a memcpy call in the more privileged splwow64 address space. The only difference between CVE-2020-0986 and CVE-2019-0880, which was also exploited in-the-wild, is that CVE-2019-0880 exploited the memcpy by sending message type 0x75 and CVE-2020-0986 exploits it by sending message type 0x6D.

From this great write-up from ByteRaptors on CVE-2019-0880 the pseudo code that allows the controlling of the memcpy is:

The equivalent pseudocode for CVE-2020-0986 is below. Only the message type (0x75 to 0x6D) and the offsets of the controlled memcpy arguments changed as highlighted below.

In addition to CVE-2020-0986 being a trivial variant of a previous in-the-wild vulnerability, CVE-2020-0986 was also not patched completely and the vulnerability was still exploitable even after the patch was applied. This is detailed in the “Exploited 0-days not properly fixed” section below.

Freetype CVE-2020-15999

In October 2020, Project Zero discovered multiple exploit chains being used in the wild. The exploit chains targeted iPhone, Android, and Windows users, but they all shared the same Freetype RCE to exploit the Chrome renderer, CVE-2020-15999. The vulnerability is a heap buffer overflow in the Load_SBit_Png function. The vulnerability was being triggered by an integer truncation. `Load_SBit_Png` processes PNG images embedded in fonts. The image width and height are stored in the PNG header as 32-bit integers. Freetype then truncated them to 16-bit integers. This truncated value was used to calculate the bitmap size and the backing buffer is allocated to that size. However, the original 32-bit width and height values of the bitmap are used when reading the bitmap into its backing buffer, thus causing the buffer overflow.

In November 2014, Project Zero team member Mateusz Jurczyk reported CVE-2014-9665 to Freetype. CVE-2014-9665 is also a heap buffer overflow in the Load_SBit_Png function. This one was triggered differently though. In CVE-2014-9665, when calculating the bitmap size, the size variable is vulnerable to an integer overflow causing the backing buffer to be too small.

To patch CVE-2014-9665, Freetype added a check to the rows and width prior to calculating the size as shown below.

To patch CVE-2020-15999, the vulnerability exploited in the wild in 2020, this check was moved up earlier in the `Load_Sbit_Png` function and changed to `imgHeight` and `imgWidth`, the width and height values that are included in the header of the PNG.

To summarize:

• CVE-2014-9665 caused a buffer overflow by overflowing the size field in the size = map->rows * map->pitch; calculation.
• CVE-2020-15999 caused a buffer overflow by truncating metrics->width and metrics->height which are then used to calculate the size field, thus causing the size field to be too small.

A fix for the root cause of the buffer overflow in November 2014 would have been to bounds check imgWidth and imgHeight prior to any assignments to an unsigned short. Including the bounds check of the height and widths from the PNG headers early would have prevented both manners of triggering this buffer overflow.

Apple Safari CVE-2020-27930

This vulnerability is slightly different than the rest in that while it’s still a variant, it’s not clear that by current disclosure norms, one would have necessarily expected Apple to have picked up the patch. Apple and Microsoft both forked the Adobe Type Manager code over 20 years ago. Due to the forks, there’s no true “upstream”. However when vulnerabilities were reported in Microsoft’s, Apple’s, or Adobe’s fork, there is a possibility (though no guarantee) that it was also in the others.

CVE-2020-27930 vulnerability was used in an exploit chain for iOS. The variant, CVE-2015-0993, was reported to Microsoft in November 2014. In CVE-2015-0993, the vulnerability is in the blend operator in Microsoft’s implementation of Adobe’s Type 1/2 Charstring Font Format. The blend operation takes n + 1 parameters. The vulnerability is that it did not validate or handle correctly when n is negative, allowing the font to arbitrarily read and write on the native interpreter stack.

CVE-2020-27930, the vulnerability exploited in-the-wild in 2020, is very similar. The vulnerability this time is in the callothersubr operator in Apple’s implementation of Adobe’s Type 1 Charstring Font Format. In the same way as the vulnerability reported in November 2014, callothersubr expects n arguments from the stack. However, the function did not validate nor handle correctly negative values of n, leading to the same outcome of arbitrary stack read/write.

Six years after the original vulnerability was reported, a similar vulnerability was exploited in a different project. This presents an interesting question: How do related, but separate, projects stay up-to-date on security vulnerabilities that likely exist in their fork of a common code base? There’s little doubt that reviewing the vulnerability Microsoft fixed in 2015 would help the attackers discover this vulnerability in Apple.

Exploited 0-days not properly fixed… 😭

Three vulnerabilities that were exploited in-the-wild were not properly fixed after they were reported to the vendor.

Internet Explorer JScript CVE-2020-0674

In the section above, we detailed the timeline of the Internet Explorer JScript vulnerabilities that were exploited in-the-wild. After the most recent vulnerability, CVE-2020-0674, was exploited in January 2020, it still didn’t comprehensively fix all of the variants. Microsoft patched CVE-2020-0968 in April 2020. We show the trigger in the section above.

Google Chrome CVE-2019-13674

CVE-2019-13674 in Chrome is an interesting case. When it was patched in November 2019, it was not known to be exploited in-the-wild. Instead, it was reported by security researchers Soyeon Park and Wen Xu. Three months later, in February 2020, Sergei Glazunov of Project Zero discovered that it was exploited in-the-wild, and may have been exploited as a 0-day prior to the patch. When Sergei realized it had already been patched, he decided to look a little closer at the patch. That’s when he realized that the patch didn’t fix all of the paths to trigger the vulnerability. To read about the vulnerability and the subsequent patches in greater detail, check out Sergei’s blog post, “Chrome Infinity Bug”.

To summarize, the vulnerability is a type confusion in Chrome’s v8 Javascript engine. The issue is in the function that is designed to compute the type of induction variables, the variable that gets increased or decreased by a fixed amount in each iteration of a loop, such as a for loop. The algorithm works only on v8’s integer type though. The integer type in v8 includes a few special values, +Infinity and -Infinity. -0 and NaN do not belong to the integer type though. Another interesting aspect to v8’s integer type is that it is not closed under addition meaning that adding two integers doesn’t always result in an integer. An example of this is +Infinity + -Infinity = NaN.

Therefore, the following line is sufficient to trigger CVE-2019-13674. Note that this line will not show any observable crash effects and the road to making this vulnerability exploitable is quite long, check out this blog post if you’re interested!

The patch that Chrome released for this vulnerability added an explicit check for the NaN case. But the patch made an assumption that leads to it being insufficient: that the loop variable can only become NaN if the sum or difference of the initial value of the variable and the increment is NaN. The issue is that the value of the increment can change inside the loop body. Therefore the following trigger would still work even after the patch was applied.

To “revive” the entire exploit, the attacker only needed to change a couple of lines in the trigger to have another working 0-day. This incomplete fix was reported to Chrome in February 2020. This patch was more conservative: it bailed as soon as the type detected that increment can be +Infinity or -Infinity.

Unfortunately, this patch introduced an additional security vulnerability, which allowed for a wider choice of possible “type confusions”. Again, check out Sergei’s blog post if you’re interested in more details.

This is an example where the exploit is found after the bug was initially reported by security researchers. As an aside, I think this shows why it’s important to work towards “correct & comprehensive” patches in general, not just vulnerabilities known to be exploited in-the-wild. The security industry knows there is a detection gap in our ability to detect 0-days exploited in-the-wild. We don’t find and detect all exploited 0-days and we certainly don’t find them all in a timely manner.

Windows splwow64 CVE-2020-0986

This vulnerability has already been discussed in the previous section on variants. After Kaspersky reported that CVE-2020-0986 was actively exploited as a 0-day, I began performing root cause analysis and variant analysis on the vulnerability. The vulnerability was patched in June 2020, but it was only disclosed as exploited in-the-wild in August 2020.

Microsoft’s patch for CVE-2020-0986 replaced the raw pointers that an attacker could previously send through the LPC message, with offsets. This didn’t fix the root cause vulnerability, just changed how an attacker would trigger the vulnerability. This issue was reported to Microsoft in September 2020, including a working trigger. Microsoft released a more complete patch for the vulnerability in January 2021, four months later. This new patch checks that all memcpy operations are only reading from and copying into the buffer of the message.

Correct and comprehensive patches

We’ve detailed how six 0-days that were exploited in-the-wild in 2020 were closely related to vulnerabilities that had been seen previously. We also showed how three vulnerabilities that were exploited in-the-wild were either not fixed correctly or not fixed comprehensively when patched this year.

When 0-day exploits are detected in-the-wild, it’s the failure case for an attacker. It’s a gift for us security defenders to learn as much as we can and take actions to ensure that that vector can’t be used again. The goal is to force attackers to start from scratch each time we detect one of their exploits: they’re forced to discover a whole new vulnerability, they have to invest the time in learning and analyzing a new attack surface, they must develop a brand new exploitation method. To do that, we need correct and comprehensive fixes.

Being able to correctly and comprehensively patch isn't just flicking a switch: it requires investment, prioritization, and planning. It also requires developing a patching process that balances both protecting users quickly and ensuring it is comprehensive, which can at times be in tension. While we expect that none of this will come as a surprise to security teams in an organization, this analysis is a good reminder that there is still more work to be done. 

Exactly what investments are likely required depends on each unique situation, but we see some common themes around staffing/resourcing, incentive structures, process maturity, automation/testing, release cadence, and partnerships.

While the aim is that one day all vulnerabilities will be fixed correctly and comprehensively, each step we take in that direction will make it harder for attackers to exploit 0-days.

In 2021, Project Zero will continue completing root cause and variant analyses for vulnerabilities reported as in-the-wild. We will also be looking over the patches for these exploited vulnerabilities with more scrutiny. We hope to also expand our work into variant analysis work on other vulnerabilities as well. We hope more researchers will join us in this work. (If you’re an aspiring vulnerability researcher, variant analysis could be a great way to begin building your skills! Here are two conference talks on the topic: my talk at BluehatIL 2020 and Ki Chan Ahn at OffensiveCon 2020.)

In addition, we would really like to work more closely with vendors on patches and mitigations prior to the patch being released. We often have ideas of how issues can be addressed. Early collaboration and offering feedback during the patch design and implementation process is good for everyone. Researchers and vendors alike can save time, resources, and energy by working together, rather than patch diffing a binary after release and realizing the vulnerability was not completely fixed.

Posted By Samuel Groß, Project Zero

On December 20, Citizenlab published “The Great iPwn”, detailing how “Journalists [were] Hacked with Suspected NSO Group iMessage ‘Zero-Click’ Exploit”. Of particular interest is the following note: “We do not believe that [the exploit] works against iOS 14 and above, which includes new security protections''. Given that it is also now almost exactly one year ago since we published the Remote iPhone Exploitation blog post series, in which we described how an iMessage 0-click exploit can work in practice and gave a number of suggestions on how similar attacks could be prevented in the future, now seemed like a great time to dig into the security improvements in iOS 14 in more detail and explore how Apple has hardened their platform against 0-click attacks.

The content of this blog post is the result of a roughly one-week reverse engineering project, mostly performed on a M1 Mac Mini running macOS 11.1, with the results, where possible, verified to also apply to iOS 14.3, running on an iPhone XS. Due to the nature of this project and the limited timeframe, it is possible that I have missed some relevant changes or made mistakes interpreting some results. Where possible, I’ve tried to describe the steps necessary to verify the presented results, and would appreciate any corrections or additions.

The blog post will start with an overview of the major changes Apple implemented in iOS 14 which affect the security of iMessage. Afterwards, and mostly for the readers interested in the technical details, each of the major improvements is described in more detail while also providing a walkthrough of how it was reverse engineered. At least for the technical details, it is recommended to briefly review the blog post series from last year for a basic introduction to iMessage and the exploitation techniques used to attack it.

Overview

Memory corruption based 0-click exploits typically require at least the following pieces:

1. A memory corruption vulnerability, reachable without user interaction and ideally without triggering any user notifications
2. A way to break ASLR remotely
3. A way to turn the vulnerability into remote code execution
4. (Likely) A way to break out of any sandbox, typically by exploiting a separate vulnerability in another operating system component (e.g. a userspace service or the kernel)

With iOS 14, Apple shipped a significant refactoring of iMessage processing, and made all four parts of the attack harder. This is mainly due to three central changes:

1. The BlastDoor Service

One of the major changes in iOS 14 is the introduction of a new, tightly sandboxed “BlastDoor” service which is now responsible for almost all parsing of untrusted data in iMessages (for example, NSKeyedArchiver payloads). Furthermore, this service is written in Swift, a (mostly) memory safe language which makes it significantly harder to introduce classic memory corruption vulnerabilities into the code base.

The following diagram shows the rough new iMessage processing pipeline, with the name of the respective service process shown at the top of each box.

As can be seen, the majority of the processing of complex, untrusted data has been moved into the new BlastDoor service. Furthermore, this design with its 7+ involved services allows fine-grained sandboxing rules to be applied, for example, only the IMTransferAgent and apsd processes are required to perform network operations. As such, all services in this pipeline are now properly sandboxed (with the BlastDoor service arguably being sandboxed the strongest).

2. Re-randomization of the Dyld Shared Cache Region

Historically, ASLR on Apple’s platforms had one architectural weakness: the shared cache region, containing most of the system libraries in a single prelinked blob, was only randomized per boot, and so would stay at the same address across all processes. This turned out to be especially critical in the context of 0-click attacks, as it allowed an attacker, able to remotely observe process crashes (e.g. through timing of automatic delivery receipts), to infer the base address of the shared cache and as such break ASLR, a prerequisite for subsequent exploitation steps.

However, with iOS 14, Apple has added logic to specifically detect this kind of attack, in which case the shared cache is re-randomized for the targeted service the next time it is started, thus rendering this technique useless. This should make bypassing ASLR in a 0-click attack context significantly harder or even impossible (apart from brute force) depending on the concrete vulnerability.

3. Exponential Throttling to Slow Down Brute Force Attacks

To limit an attacker’s ability to retry exploits or brute force ASLR, the BlastDoor and imagent services are now subject to a newly introduced exponential throttling mechanism enforced by launchd, causing the interval between restarts after a crash to double with every subsequent crash (up to an apparent maximum of 20 minutes). With this change, an exploit that relied on repeatedly crashing the attacked service would now likely require in the order of multiple hours to roughly half a day to complete instead of a few minutes.

The remainder of this blog post will now look at each of these three changes in greater depths.

The BlastDoor Service

The new BlastDoor service and its role in the processing of iMessages can be studied by following the flow of an incoming iMessage. On the wire, a simple text iMessage would look something like this, encoded as binary plist:

{

    // Group UUID

    gid = "008412B9-A4F7-4B96-96C3-70C4276CB2BE";

    // Group protocol version

    gv = 8;

    // Chat participants

    p =     (

        "mailto:[email protected]",

        "mailto:[email protected]"

    );

    // Participants version

    pv = 0;

    // Message being replied to, usually the last message in the chat 

    r = "6401430E-CDD3-4BC7-A377-7611706B431F";

    // The plain text content

    t = "Hello World!";

    // Probably some other version number

    v = 1;

    // The rich text content    

    x = "<html><body>Hello World!</body></html>";  

}

As such, the minimal steps required to parse it are:

1. If necessary, decompress the binary data
2. Decode the plist from its binary serialization format
3. Extract its various fields and ensure they have the correct type
4. Decode the `x` key if present, using an XML decoder

Previously, all of this work happened in imagent. With iOS 14, however, it all moved into the new BlastDoor service. While the main processing flow still starts in imagent, which receives the raw but unencrypted payload bytes from identityservicesd (part of the IDS framework) in -[IMDiMessageIDSDelegate service:account:incomingTopLevelMessage:fromID:messageContext:], messages are then more or less immediately forwarded to the BlastDoor service through +[IMBlastdoor sendDictionary:withCompletionBlock:] which creates the reply handler block and then calls -[IMMessagesBlastDoorInterface diffuseTopLevelDictionary:resultHandler:]. At that point processing ends up in Swift code that deserializes the binary payload and sends it to the BlastDoor service over XPC.

Inside BlastDoor, the work mostly happens in BlastDoor.framework and MessagesBlastDoorService. As most of it is written in Swift, it is fairly unpleasant to statically reverse engineer it (no symbols, many virtual calls, swift runtime code sprinkled all over the place), but fortunately, that is also not really necessary for the purpose of this blog post. However, it is worth noting that while the high level control flow logic is written in Swift, some of the parsing steps still involve the existing ObjectiveC or C implementations. For example, XML is being parsed by libxml, and the NSKeyedArchiver payloads by the ObjectiveC implementation of NSKeyedUnarchiver.

The responses from BlastDoor can be seen by breaking on the reply handler function in imagent (the function can be found in +[IMBlastdoor sendDictionary:withCompletionBlock:] or by searching for XREFs to the string “Blastdoor response %p received (command: %hhu, guid: %@)” in IMDaemonCore.framework). A typical BlastDoor response for a simple text message is shown below:

(lldb) po $x2

TextMessage(

    metadata: BlastDoor.Metadata(

        messageGUID: D391CC96-9CC6-44C6-B827-1DEB0F252529,

        timestamp: Optional(1610108299117662350),

        wantsDeliveryReceipt: true,

        wantsCheckpointing: false,

        storageContext: BlastDoor.Metadata.StorageContext(

            isFromStorage: false, isLastFromStorage: false

        )

    ),

    messageSubType: MessageType.textMessage(BlastDoor.Message(

        plainTextBody: Optional("Hello World"),

        plainTextSubject: nil,

        content: Optional(BlastDoor.AttributedString(

            attributes: [

                BlastDoor.BaseWritingDirectionAttribute(

                    range: Range(0..<11), direction: WritingDirection.natural

                ),

                BlastDoor.MessagePartAttribute(

                    range: Range(0..<11), partNumber: 0

                )

            ],

            string: "Hello World"

        )),

        _participantDestinationIdentifiers: [

            "mailto:[email protected]",

            "mailto:[email protected]"

        ],

        attributionInfo: []

    )),

    encryptionType: BlastDoor.TextMessage.EncryptionType.pair_ec,

    replyToGUID: Optional(6401430E-CDD3-4BC7-A377-7611706B431F),

    _threadIdentifierGUID: nil,

    _expressiveSendStyleIdentifier: nil,

    _groupID: Optional("008412B9-A4F7-4B96-96C3-70C4276CB2BE"),

    currentGroupName: nil,

    groupParticipantVersion: Optional(0),

    groupProtocolVersion: Optional(8),

    groupPhotoCreationTime: nil,

    messageSummaryInfo: nil,

    nicknameInformation: nil,

    truncatedNicknameRecordKey: nil

)

One can roughly associate every field in this data structure with parts of the on-wire iMessage format. For example, the plainTextBody field contains the content of the `t` field, while the content field corresponds to the content of the `x` field.

Besides simple text messages, iMessages can additionally contain attachments (essentially arbitrary files which are encrypted and temporarily uploaded to iCloud) as well as rather complex serialized NSKeyedArchiver archives, which have been the source of bugs in the past.

For these types of iMessages, the following additional parsing steps are necessary:

1. Unpack attachment metadata (NSKeyedArchiver format)
2. Download attachments from iCloud server
3. Deserialize NSKeyedArchiver plugin archives and generate a preview for the notification

As an example, consider what happens when a user sends a link to a website over iMessage. In that case, the sending device will first render a preview of the webpage and collect some metadata about it (such as the title and page description), then pack those fields into an NSKeyedArchiver archive. This archive is then encrypted with a temporary key and uploaded to the iCloud servers. Finally, the link as well as the decryption key are sent to the receiver as part of the iMessage. In order to create a useful user notification about the incoming iMessage, this data has to be processed by the receiver on a 0-click code path. As that again involves a fair amount of complexity, it is also done inside BlastDoor: after receiving the BlastDoor reply from above and realizing that the message contains an attachment, imagent first instructs IMTransferAgent to download and decrypt the iCloud attachment. Afterwards, it will call into -[IMTranscodeController decodeiMessageAppPayload:bundleID:completionBlock:blockUntilReply:] which forwards the relevant data to the IMTranscoderAgent, which then proceeds into +[IMAttachmentBlastdoor sendBalloonPluginPayloadData:withBundleIdentifier:completionBlock:] and finally calls -[IMMessagesBlastDoorInterface defuseBalloonPluginPayload:withIdentifier:resultHandler:].

In the BlastDoor service, the plugin data decoding is then again performed in Swift, and dispatched to the corresponding plugin type, as determined by the plugin id. For RichLinks (plugin id com.apple.messages.URLBalloonProvider), processing ends up in LinkPresentation.MessagesPayload.init(dataRepresentation:), which deserializes the NSKeyedArchiver payload and to extract the preview image and URL metadata from it in order to generate a preview message.

Sandboxing

The sandbox profile can be found in System/Library/Sandbox/Profiles/blastdoor.sb and is also attached at the end of this blog post. It appears to be identical on iOS and macOS. The profile can be studied statically, and for that purpose is attached at the bottom of this blogpost, or dynamically, for example by using the sandbox-exec tool:

> echo "(allow process-exec (literal \"$(pwd)/test\"))" >> ./blastdoor.sb

> clang -o test test.c   # try to open files, network connections, etc.

> sandbox-exec -f ./blastdoor.sb ./test

The sandbox profile states:

;;; This profile contains the rules necessary to make BlastDoor as close to

;;; compute-only as possible, while still remaining functional.

And indeed, the sandbox profile is quite tight:

• only a handful of local IPC services, namely diagnosticd, logd, opendirectoryd, syslogd, and notifyd, can be reached
• almost all file system interaction is blocked
• any interaction with IOKit drivers (historically a big source of vulnerabilities) is forbidden
• outbound network access is denied

Furthermore, the profile makes use of syscall filtering to restrict the interactions with the core kernel. However, as of now the syscall filter seems to be in “permissive” mode:

;; To be uncommented once the system call whitelist is complete...

;; (deny syscall-unix (with send-signal SIGKILL))

As such, the BlastDoor service is still allowed to perform any syscall, but it is to be expected that the syscall filtering will soon be put into “enforcement mode”, which would further boost its effectiveness.

Crash Monitoring?

An interesting side effect of the new processing pipeline is that imagent is now able to detect when an incoming message caused a crash in BlastDoor (it will receive an XPC error). Even more interesting is the fact that imagent appears to be informing Apple’s servers about such events, as can be seen by setting a breakpoint on -[APSConnectionServer handleSendOutgoingMessage:] in apsd, the daemon responsible for implementing Apple’s push services (on top of which iMessage is built). Displaying the outgoing message will show the following:

(lldb) po [$x2 dictionaryRepresentation]

{

    APSCritical = 1;

    APSMessageID = 543;

    APSMessageIdentifier = 1520040396;

    APSMessageTopic = "com.apple.madrid";

    APSMessageUserInfo =     {

        c = 115;

        fR = 13500;

        fRM = "c-100-BlastDoor.Explosion-1-com.apple.BlastDoor.XPC-ServiceCrashed";

        fU = {length = 16, bytes = 0x3a4912626c9645f98cb26c7c2d439520};

        i = 1520040396;

        nr = 1;

        t = {length = 32, bytes = ... };

        ua = "[macOS,11.1,20C69,Macmini9,1]";

        v = 7;

    };

    APSOutgoingMessageSenderTokenName = 501;

    APSPayloadFormat = 1;

    APSTimeout = 120;

    APSTimestamp = "2021-01-06 19:52:10 +0000";

}

As can be seen, imagent is apparently informing the iMessage servers that the message with the UUID 0x3a4912626c9645f98cb26c7c2d439520 (fU key) has caused a crash in BlastDoor.

It is unclear what the purpose of this is without access to the server’s code. While these notifications may simply be used for statistical purposes, they would also give Apple a fairly clear signal about attacks against iMessage involving brute-force and a somewhat weaker signal about any failed exploits against the BlastDoor service.

In my experiments, after observing one of these crash notifications, the server would start directly sending delivery receipts to the sender for messages that hadn't actually been processed by the receiver yet. Possibly this is another, independent effort to break the crash oracle technique by confusing the sender, but that is hard to verify without access to the code running on the server. In any case, it is worth noting that this “spoofing” of delivery receipts by the server is generally possible as the message UUID, which is more or less the only content of a delivery receipt, is part of the non-end2end encrypted payload and is thus known to the server (break on -[APSConnectionServer handleSendOutgoingMessage:] and inspect outgoing iMessages to verify this, the UUID will be in the U key, while the e2e-encrypted data will be in the P key). This is most likely necessary so the server can track which messages have already been delivered and which ones it still needs to keep around for delivery in the future.

Shared Cache Resliding

Previously, when exploiting an iMessage memory corruption bug, a “crash oracle” could be used to reveal the location of the shared cache region in memory: the attacker would trigger the memory corruption bug in a way that would cause an access to a memory location somewhere in the region 0x180000000 - 0x280000000 (where the shared cache can be mapped). If the memory was valid, no crash would occur and imagent would then send a delivery receipt to the attacker. However, If a crash occurred, no such receipt would be delivered, informing the attacker that the address was unmapped. Through clever selection of the queried addresses, the location of the shared cache could be revealed in logarithmic time, with only about 20 messages.

However, with iOS 14 Apple has added a mechanism to re-randomize the location of the shared cache region for an “attacked” process, thus breaking a fundamental assumption of this technique and rendering it ineffective. This is significant as the crash oracle technique was one of very few, if not the only, fairly generic ASLR bypass techniques usable in 0-click iMessage attacks.

To understand how the shared cache resliding works, one can start by looking at the kernel. In iOS 14, the kernel can now have two active shared cache regions: the “regular” region and a “reslided” region. During an attack, the following then happens:

1. When an attacker attempts to use a crash-oracle-based technique, the attacked process would quickly end up accessing unmapped memory in the range 0x180000000 - 0x280000000 (where the shared cache is mapped) and crashes
2. The kernel handles the segmentation fault generated by the CPU, and sets a specific flag in the crash info that signals that the crash happened inside the shared cache region
3. At the same time, the kernel will mark the currently active reslided shared cache region (if one exists) as stale, causing it to be recreated and thus re-randomized the next time it is used
4. launchd (as the parent process of the crashed service) receives the crash info, notices the OS_REASON_FLAG_SHAREDREGION_FAULT flag, and sets the ReslideSharedCache property on the service associated with the crashed process (see `launchctl procinfo $pid` and search for `reslide shared cache = 1`)
5. The next time the service is restarted, launchd then adds the POSIX_SPAWN_RESLIDE attribute for posix_spawn due to the ReslideSharedCache property
6. In the kernel, this flag now causes the newly created process to be given the reslided shared cache image. However, as no active reslided region currently exists (the previous one was marked as stale in step 3.), a new one is created at a newly randomized address.

The result of this is that whenever an attacker attempts to use a crash-oracle to break ASLR, the attacked service would receive a different shared cache region every time it is launched, thus preventing the attack from succeeding. For the time being, this feature appears to only be active on iOS though, but it would be expected to come to macOS as well.

While this mechanism would in principle also protect 3rd party apps from similar attacks, protection for those is currently somewhat weaker, likely in order to first evaluate the real-world performance impact of this change (the shared cache is a significant performance optimization of the OS). In particular, step 3 is currently only performed if the crashing process is a platform binary (essentially binaries that ship with the OS and are directly signed by Apple) such as the services handling iMessages. However, for 3rd party processes, it would only happen if the global vm_shared_region_reslide_restrict is set to zero:

/*

 * Flag to control what processes should get shared cache randomize resliding

 * after a fault in the shared cache region:

 *

 * 0 - all processes get a new randomized slide

 * 1 - only platform processes get a new randomized slide

 */

Which is controlled by the vm_shared_region_reslide_restrict bootarg. This currently seems to be set to one. In essence, for 3rd party apps this means:

1. When the attacked process first crashes, the kernel will still set the OS_REASON_FLAG_SHAREDREGION_FAULT flag, and launchd will add the ReslideSharedCache property, but the current reslided region won’t be invalidated
2. The restarted service is then restarted and now uses the “reslided” shared cache region
3. When the service crashes the next time, and if that service is the only one currently using the reslided shared cache region (which should usually be the case, but could possibly be influenced by the attacker), the region’s refcount drops to zero, and the shared cache region is marked for removal.
4. However, removal will only actually happen after two minutes. As such, if the service is restarted within two minutes, it will receive the same shared cache region at the same location in memory.

As a result, a third-party app could still be attacked through a crash-oracle technique if it automatically sends some form of delivery receipt to the sender and restarts quickly enough after a crash. This could, however, be prevented for example by enabling ExponentialThrottling for these services. Ideally, and assuming that the performance penalty is reasonable, Apple would enable re-randomization for all apps in the future.

Exponential Throttling

Another thing we suggested back in 2019 was to limit the number of attempts an attacker gets when attempting to exploit a vulnerability. This was mostly important to defend against the crash-oracle technique, but would also help to prevent brute force attacks (e.g., given enough attempts, one could simply brute force the location of the shared cache region). The new ExponentialThrottling feature in launchd seems to achieve just that.

To use it, a system daemon or agent has to opt-in by setting "_ExponentialThrottling = 1” in its Info.plist (essentially the service metadata), as can be seen below for the BlastDoor service:

> plutil -p /System/Library/PrivateFrameworks/MessagesBlastDoorSupport.framework/Versions/A/XPCServices/MessagesBlastDoorService.xpc/Contents/Info.plist

{

  "CFBundleDisplayName" => "MessagesBlastDoorService"

  "CFBundleExecutable" => "MessagesBlastDoorService"

  "CFBundleIdentifier" => "com.apple.MessagesBlastDoorService"

  ...

  "XPCService" => {

    "_ExponentialThrottling" => 1

  }

}

Apart from the BlastDoor service, it is also used for imagent:

> plutil -p /System/Library/LaunchAgents/com.apple.imagent.plist

{

  "_ExponentialThrottling" => 1

  ...

but doesn’t appear to be used for any other service, as can, for example, be seen by looking at the output of the launchctl dumpstate command, which will only show “exponential throttling = 1” for com.apple.imagent and com.apple.MessagesBlastDoorService.

Presumably, the _ExponentialThrottling property instructs launchd (the macOS and iOS init process), to delay subsequent restarts of a crashing service. While it is somewhat challenging to statically reverse engineer launchd due to the lack of source code or binary symbols, it is fortunately fairly easy to experimentally determine the impact of the _ExponentialThrottling property, for example by installing a custom daemon that writes the current timestamp to a file before crashing. By default, so without ExponentialThrottling, one would see the following:

Service started on Wed Jan  6 13:56:03 2021

Service started on Wed Jan  6 13:56:13 2021

Service started on Wed Jan  6 13:56:23 2021

Service started on Wed Jan  6 13:56:33 2021

As can be seen, by default, a service is, at the earliest, restarted ten seconds after it was previously started. However, using the following service plist which enables ExponentialThrottling:

> # Start service with

> # launchctl bootstrap system /Library/LaunchDaemons/net.saelo.test.plist

> plutil -p /Library/LaunchDaemons/net.saelo.test.plist

{

  "_ExponentialThrottling" => 1

  "KeepAlive" => 1

  "Label" => "net.saelo.test"

  "POSIXSpawnType" => "Interactive"

  "Program" => "/path/to/program"

}

One can observe the following:

Service started on Wed Jan  6 10:42:43 2021

Service started on Wed Jan  6 10:42:53 2021 (+10s)

Service started on Wed Jan  6 10:43:03 2021 (+10s)

Service started on Wed Jan  6 10:43:13 2021 (+10s)

Service started on Wed Jan  6 10:43:33 2021 (+20s)

Service started on Wed Jan  6 10:44:13 2021 (+40s)

Service started on Wed Jan  6 10:45:33 2021 (+80s)

Service started on Wed Jan  6 10:48:13 2021 (+160s [~2.5m])

Service started on Wed Jan  6 10:53:33 2021 (+320s [~5m])

Service started on Wed Jan  6 11:04:13 2021 (+640s [~10m])

Service started on Wed Jan  6 11:24:13 2021 (+20m)

Service started on Wed Jan  6 11:44:13 2021 (+20m)

Service started on Wed Jan  6 12:04:13 2021 (+20m)

Here, the exponential increase in the time between subsequent restarts is clearly visible, and goes up to an apparent maximum of 20 minutes. And indeed, launchd does contain the following bit of code in a function presumably responsible for computing the next restart delay (search for XREFs to the string "%s: service throttled by %llu seconds"):

  if ( delay >= 1200 )

    result = 1200LL;                 // 20 minutes

  else

    result = delay;

With this change, an exploit that relied on brute force would now only get one attempt every 20 minutes instead of every 10 seconds.

(Upcoming?) ObjectiveC ISA PAC

The PoC exploit against iMessage on iOS 12.4 relied heavily on faking ObjectiveC objects to gain a form of arbitrary code execution despite the presence of pointer authentication (PAC). This was mainly possible because the ISA field, containing the pointer to the Class object and thus making a piece of memory appear like a valid ObjectiveC object, was not protected through PAC and could thus be faked. With iOS 14, this now seems to be changing: while previously, the top 19 bits of the ISA value contained the inline refcount, it now appears that this field has been reduced to 9 bits (of which the LSB appears to be reserved for some purpose, leaving an 8-bit inline refcount, see the bit shifting logic in objc_release or objc_retain), while the freed-up bits now hold a PAC, as can be seen in objc_rootAllocWithZone in libobjc.dylib:

    ; Allocate the object

    BL              j__calloc_3

    CBZ             X0, loc_1953DA434

    MOV             X8, X0

    ; “Tag” the address with a constant to get a PAC modifier value

    MOVK            X8, #0x6AE1,LSL#48        

    MOV             X9, X19

    ; Compute PAC of Class pointer with tagged object address as modifier

    PACDA           X9, X8

    ; Clear top 9 bits (inline refcnt) and bottom 3 bits (other bitfields)       

    AND             X8, X9, #0x7FFFFFFFFFFFF8

    ; Set LSB and inline refcount to one

    MOV             X9, #0x100000000000001

    ORR             X9, X8, X9

    ; Presumably, the refcnt isn’t used for all types of classes...

    TST             W20, #0x2000

    CSEL            X8, X9, X8, EQ

    ; Store the resulting value into the ISA field

    STR             X8, [X0]

However, currently the ISA PAC appears to never be checked, as such, it doesn’t yet affect any exploits. The most likely reason for this is that the ISA PAC feature is being rolled out in multiple phases, with the current implementation meant to allow in-depth performance evaluation, in particular of the reduced size of the inline refcount, which will likely cause more objects to use the more expensive out-of-line refcounting (used once the inline refcount saturates). With that, it can be expected that, in the absence of major performance issues, future releases of iOS and macOS will use PAC for the ObjC ISA field, thus likely breaking exploits that have to rely on faking ObjectiveC objects to achieve arbitrary code execution.

Conclusion

This blog post discussed three improvements in iOS 14 affecting iMessage security: the BlastDoor service, resliding of the shared cache, and exponential throttling. Overall, these changes are probably very close to the best that could’ve been done given the need for backwards compatibility, and they should have a significant impact on the security of iMessage and the platform as a whole. It’s great to see Apple putting aside the resources for these kinds of large refactorings to improve end users’ security. Furthermore, these changes also highlight the value of offensive security work: not just single bugs were fixed, but instead structural improvements were made based on insights gained from exploit development work.

As for the alleged NSO iMessage exploit, it may have been prevented from working against iOS 14 by any of the following:

• The bug was fixed in iOS 14, for example due to the rewrite of large parts of the iMessage processing pipeline in Swift

• The mere fact that processing happens in a different process, which could for example break a heap layouting primitive
• The shared cache resliding would break their exploit if their exploit relied on some form of crash oracle to break ASLR
• The stronger sandbox of the BlastDoor service, which could prevent the exploitation of a privilege escalation vulnerability after compromising the BlastDoor process

While these are some possible scenarios, and it could be the case that the exploit “just” needs some re-engineering to function again, the fact that these security improvements were shipped is certainly a positive outcome.

Attachment 1: blastdoor.sb

;;; This profile contains the rules necessary to make BlastDoor as close to

;;; compute-only as possible, while still remaining functional.

;;;

;;; For all platforms: /System/Library/PrivateFrameworks/MessagesBlastDoorSupport.framework/XPCServices/MessagesBlastDoorService.xpc/MessagesBlastDoorService

(version 1)

;;; -------------------------------------------------------------------------------------------- ;;;

;;; Basic Rules

;;; -------------------------------------------------------------------------------------------- ;;;

;; Deny all default rules.

(deny default)

(deny file-map-executable process-info* nvram*)

(deny dynamic-code-generation)

;; Rules copied from system.sb. Ones that we've deemed overly permissive

;; or unnecessary for BlastDoor have been removed.

;; Allow read access to standard system paths.

(allow file-read*

       (require-all (file-mode #o0004)

                    (require-any (subpath "/System")

                                 (subpath "/usr/lib")

                                 (subpath "/usr/share")

                                 (subpath "/private/var/db/dyld"))))

(allow file-map-executable

       (subpath "/System/Library/CoreServices/RawCamera.bundle")

       (subpath "/usr/lib")

       (subpath "/System/Library/Frameworks"))

(allow file-test-existence (subpath "/System"))

(allow file-read-metadata

       (literal "/etc")

       (literal "/tmp")

       (literal "/var")

       (literal "/private/etc/localtime"))

;; Allow access to standard special files.

(allow file-read*

       (literal "/dev/random")

       (literal "/dev/urandom"))

(allow file-read* file-write-data

       (literal "/dev/null")

       (literal "/dev/zero"))

(allow file-read* file-write-data file-ioctl

       (literal "/dev/dtracehelper"))

;; TODO: Don't allow core dumps to be written out unless this is on a dev

;; fused device?

(allow file-write*

       (require-all (regex #"^/cores/")

                    (require-not (file-mode 0))))

;; Allow IPC to standard system agents.

(allow mach-lookup

       (global-name "com.apple.diagnosticd")

       (global-name "com.apple.logd")

       (global-name "com.apple.system.DirectoryService.libinfo_v1")

       (global-name "com.apple.system.logger")

       (global-name "com.apple.system.notification_center"))

;; Allow mostly harmless operations.

(allow signal process-info-dirtycontrol process-info-pidinfo

       (target self))

;; Temporarily allow sysctl-read with reporting to see if this is

;; used for anything.

(allow (with report) sysctl-read)

;; We don't need to post any darwin notifications.

(deny darwin-notification-post)

;; We shouldn't allow any other file operations not covered under

;; the default of deny above.

(deny file-clone file-link)

;; Don't deny file-test-existence: <rdar://problem/59611011>

;; (deny file-test-existence)

;; Don't allow access to any IOKit properties.

(deny iokit-get-properties)

(deny mach-cross-domain-lookup)

;; Don't allow BlastDoor to spawn any other XPC services other than

;; ones that we can intentionally whitelist later.

(deny mach-lookup (xpc-service-name-regex #".*"))

;; Don't allow any commands on sockets.

(deny socket-ioctl)

;; Denying this should have no ill effects for our use case.

(deny system-privilege)

;; To be uncommented once the system call whitelist is complete...

;; (deny syscall-unix (with send-signal SIGKILL))

(allow syscall-unix

       (syscall-number SYS_exit)

       (syscall-number SYS_kevent_qos)

       (syscall-number SYS_kevent_id)

       (syscall-number SYS_thread_selfid)

       (syscall-number SYS_bsdthread_ctl)

       (syscall-number SYS_kdebug_trace64)

       (syscall-number SYS_getattrlist)

       (syscall-number SYS_sigsuspend_nocancel)

       (syscall-number SYS_proc_info)

       (syscall-number SYS___disable_threadsignal)

       (syscall-number SYS___pthread_sigmask)

       (syscall-number SYS___mac_syscall)

       (syscall-number SYS___semwait_signal_nocancel)

       (syscall-number SYS_abort_with_payload)

       (syscall-number SYS_access)

       (syscall-number SYS_bsdthread_create)

       (syscall-number SYS_bsdthread_terminate)

       (syscall-number SYS_close)

       (syscall-number SYS_close_nocancel)

       (syscall-number SYS_connect)

       (syscall-number SYS_csops_audittoken)

       (syscall-number SYS_csrctl)

       (syscall-number SYS_fcntl)

       (syscall-number SYS_fsgetpath)

       (syscall-number SYS_fstat64)

       (syscall-number SYS_fstatfs64)

       (syscall-number SYS_getdirentries64)

       (syscall-number SYS_geteuid)

       (syscall-number SYS_getfsstat64)

       (syscall-number SYS_getgid)

       (syscall-number SYS_getrlimit)

       (syscall-number SYS_getuid)

       (syscall-number SYS_ioctl)

       (syscall-number SYS_issetugid)

       (syscall-number SYS_lstat64)

       (syscall-number SYS_madvise)

       (syscall-number SYS_mmap)

       (syscall-number SYS_munmap)

       (syscall-number SYS_mprotect)

       (syscall-number SYS_mremap_encrypted)

       (syscall-number SYS_open)

       (syscall-number SYS_open_nocancel)

       (syscall-number SYS_openat)

       (syscall-number SYS_pathconf)

       (syscall-number SYS_pread)

       (syscall-number SYS_read)

       (syscall-number SYS_readlink)

       (syscall-number SYS_shm_open)

       (syscall-number SYS_socket)

       (syscall-number SYS_stat64)

       (syscall-number SYS_statfs64)

       (syscall-number SYS_sysctl)

       (syscall-number SYS_sysctlbyname)

       (syscall-number SYS_workq_kernreturn)

       (syscall-number SYS_workq_open)

)

;; Still allow the system call but report in log.

(allow (with report) syscall-unix)

;; For validating the entitlements of clients. This is so only entitled

;; clients can pass data into a BlastDoor instance.

(allow process-info-codesignature)

;;; -------------------------------------------------------------------------------------------- ;;;

;;; Reading Files

;;; -------------------------------------------------------------------------------------------- ;;;

;; Support for BlastDoor receiving sandbox extensions from clients to either read files, or

;; write to a target location.

;; com.apple.app-sandbox.read

(allow file-read*

       (extension "com.apple.app-sandbox.read"))

;; com.apple.app-sandbox.read-write

(allow file-read* file-write*

       (extension "com.apple.app-sandbox.read-write"))

McAfee’s Advanced Threat Research team just completed its second annual capture the flag (CTF) contest for internal employees. Based on tremendous internal feedback, we’ve decided to open it up to the public, starting with a set of challenges we designed in 2019.  

We’ve done our best to minimize guesswork and gimmicks and instead of flashy graphics and games, we’ve distilled the kind of problems we’ve encountered many times over the years during our research projects. Additionally, as this contest is educational in nature, we won’t be focused as much on the winners of the competition. The goal is for anyone and everyone to learn something new. However, we will provide a custom ATR challenge coin to the top 5 teams (one coin per team). All you need to do is score on 2 or more challenges to be eligible. When registering for the contest, make sure to use a valid email address so we can contact you.  

The ATR CTF will open on Friday, February 5th at 12:01pm PST and conclude on Thursday, February 18th, at 11:59pm PST.  

Click Here to Register! 

​​​​​​​If you’ve never participated in a CTF before, the concept is simple. You will: 

• Choose the type of challenge you want to work on, 
• Select a difficulty level by point value, 
• Solve the challenge to find a ‘flag,’ and 
• Enter the flag for the corresponding points.​​​​​

NOTE: The format of all flags is ATR[], placing the flag,  between the square brackets. For example: ATR[1a2b3c4d5e]. The flag must be submitted in full, including the ATR and square bracket parts.
 

The harder the challenge, the higher the points!  Points range from 100 to 500. All CTF challenges are designed to practice real-world security concepts, and this year’s categories include: 

• Reverse engineering 
• Exploitation 
• Web 
• Hacking Tools 
• Crypto 
• RF (Radio Frequency) 
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Following Google TAG announcement that a few profiles on twitter, were part of an APT campaign targeting security Researchers. According to Google TAG, these threat actors are North Koreans and they had multiple goals of establishing credibility by publishing a well thought of blog posts as well as interacting with researchers via Direct Messages and lure them to download and run an infected Visual Studio project.

Some of the fake profiles were: @z0x55g, @james0x40, @br0vvnn, @BrownSec3Labs

Using a Chrome 0day to infect clients?

In their post, Google TAG, mentioned that the attackers were able to pop a fully patched Windows box running Chrome. 

From Google’s post:

In addition to targeting users via social engineering, we have also observed several cases where researchers have been compromised after visiting the actors’ blog. In each of these cases, the researchers have followed a link on Twitter to a write-up hosted on blog.br0vvnn[.]io, and shortly thereafter, a malicious service was installed on the researcher’s system and an in-memory backdoor would begin beaconing to an actor-owned command and control server. At the time of these visits, the victim systems were running fully patched and up-to-date Windows 10 and Chrome browser versions.

Attacking Mobile Users?

According to ZecOps Mobile Threat Intelligence, the same threat actor might have used an Android 0day too.

If you entered this blog from your Android or iOS devices – we would like to examine your device using ZecOps Mobile DFIR tool to gather additional evidence.
Please contact us as soon as convenient at [email protected]

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Posted by James Forshaw, Project Zero

This blog is a continuation of my series of Windows exploitation tricks. This one describes an exploitation trick I’ve been trying to develop for years, succeeding (mostly, more on that later) on the latest versions of Windows 10. It’s a trick to trap access to virtual memory, get feedback when it occurs and delay access indefinitely. The blog will go into some of the background for why this technique is useful, an overview of the research I did to find the trick as well as an overview of the types of vulnerabilities it can be used with.

Background

When would you need such an exploitation trick? A good example of the types of security vulnerabilities which can benefit can be found in the seminal Bochspwn research by Mateusz Jurczyk and Gynvael Coldwind. The research showed a way of automating the discovery of memory double-fetches in the Windows kernel.

If you’ve not read the paper, a double-fetch is a type of Time-of-Check Time-of-Use (TOCTOU) vulnerability where code reads a value from memory, such as a buffer length, verifies that value is within bounds and then rereads the value from memory before use. By swapping the value in memory between the first and second fetches the verification is bypassed which can lead to security issues such as privilege escalation or information disclosure. The following is a simple example of a double fetch taken from the original paper.

This code copies a buffer from a controlled user mode address into a fixed sized stack buffer. The buffer starts with a DWORD size value which indicates the total size of the buffer. Memory corruption can occur if the size value pointed to by lpInputBuffer changes between the first read of the size value to compare against the buffer size ① and the second read of the size when copying into the buffer ②. For example, if the first time the value is read it’s 100 and the second it’s 400 then the code will pass the size check as 100 is less than 256 but will then copy 400 bytes into that buffer corrupting the stack.

Once a vulnerability such as this example was discovered Mateusz and Gynvael needed to exploit it. How they achieved exploitation is detailed in section 4 of the paper. The exploit techniques that were identified were all probabilistic. Exploitation typically required two threads racing each other, with one reading and one writing. The probabilistic nature of success is due to the probability that in between the first read from a memory location and the second read the writing thread sets a new value which exploits the vulnerability.

To widen the TOCTOU window many of the techniques described abuse the behavior of virtual memory on Windows. A process on Windows can typically access a large virtual memory region up to 8TiB size. This size is likely to be significantly larger than the physical memory in the system, especially considering the limit is per-process, not per-system. Therefore to maintain the illusion of such a large memory address space the kernel uses on-demand memory paging.

When memory is allocated in the process the CPU’s page tables are set up to indicate the presence of the memory region but are marked as invalid. At this point the virtual memory region has been allocated but there is no physical memory backing it. When the process tries to access that memory region the CPU will generate an exception, generally referred to as a page-fault, which is handled by the kernel.

The kernel can look up the memory address which was accessed to cause the page-fault and try and fix the address. How the page-fault is fixed depends on the type of memory access. A simple example is if the memory was allocated but not yet used the kernel will get a physical memory page, initialize it to zeros then adjust the page tables to map that new physical memory page at the faulting address. Once the page-fault has been fixed the faulting thread can be restarted at the instruction which accessed the memory and the memory access should now succeed as if it was always present.

A more complex scenario is if the page is part of a memory mapped file. In this case the kernel will need to request that the page’s data is read back from disk before it can satisfy the page-fault. This can take quite a long time, at least for spinning rust disks, so it might require the faulting thread to be suspended while it waits for the page to be read. Once the page has been read the memory can be fixed up, the original thread can be resumed and the thread restarted at the faulting instruction.

The end result is it can take a significant amount of time, relative to a CPU’s native speed that is, to handle a page-fault. However, abusing these virtual memory behaviors only widens the TOCTOU window, it didn’t allow for precise timing to swap values in memory. The result is the exploitation techniques still came with limitations. For example, it was very slow if not impossible in some cases to exploit on a machine with a single CPU core as it relies on having concurrent threads reading and writing.

An ideal exploit primitive would be one where the exploitation window can be made arbitrarily large so that it becomes trivial to win the race. Taking previous experience and knowledge of existing bug classes my ideal primitive would be one which meets a set of criteria:

• Works on a default installation of Windows 10 20H2.
• Gives a clear signal when memory is read or written.
• Works when memory is accessed from both user and kernel mode.
• Allows for delaying memory access indefinitely.
• The data in the memory accessed is arbitrary.
• The primitive can be set up from a range of privilege levels.
• Can trap multiple times during the same exploit.

While meeting all these criteria would be ideal, there’s no guarantee we’ll meet all or any of them. If we only meet some then the range of exploitation vulnerabilities might be limited. Let’s start with a quick overview of the existing work which might give us an idea of how to proceed to find a primitive.

Existing Work

Having spoken to Mateusz and made an effort to look for any subsequent work there seems to be little novel work over and above the original Bochspwn paper on the exploitation of these types of TOCTOU issues. At least this is true for exploitation on Windows, however, novel techniques have been developed on other platforms, specifically Linux. Both of these techniques rely on the behavior of virtual memory I previously described.

The first technique in Linux makes use of Userfault File Descriptor (userfaultfd) to get notifications when page-faults occur in a process. With userfaultfd enabled a secondary thread in the process can read a notification and handle the page-fault in user mode. Handling the fault could be mapping memory at the appropriate location or changing page protection. The key is the faulting thread is suspended until the page-fault is handled by another thread. Therefore if a kernel function accessed the memory the request will be trapped until it's completed. This allows for a primitive where the memory access can be delayed indefinitely as well as having a timing signal for the access. Using userfaultfd also allows the fault to be distinguished between read and write faults as the memory page can be write-protected

Using userfaultdd works for in-process access such as from the kernel, but is not really useful if the code accessing the memory is in another process. To solve that problem you can use the FUSE file system as Jann Horn demonstrated in a previous Project Zero blog post. A FUSE file system is implemented entirely in user mode, but any requests for the file go through the Linux kernel’s Virtual File System APIs. As a file is accessed as if it was implemented by an in-kernel file system it’s possible to map that file into memory using mmap. When a page-fault occurs on a FUSE backed memory region a request will be made to the user-mode file system daemon which can delay the read or write request indefinitely.

Remote File Systems

As far as I can tell there’s nothing equivalent to Linux’s userfaultd on Windows. One feature which caught my eye was memory write watches. But those seem to just allow an application to query if memory had been written to since the last time it was checked and doesn’t allow memory writes to be trapped.

If we can’t just trap page-faults to virtual memory what about mapping a file on a user-mode filesystem like FUSE? Unfortunately there is no built-in FUSE driver in Windows 10 (yet?), but that doesn’t mean there’s no mechanism to implement a file system in user-mode. There are some efforts to make a real FUSE on Windows, such as the WinFsp project, but I’d expect the chances of them being installed on a real system to be vanishingly small.

The first thought I had was to try to exploit Multiple UNC Provider (MUP) clients. When you access a file via a UNC path, e.g. \\server\share\file.bin, this will be handled by a MUP driver in the kernel, which will pass it to one of the registered client drivers. As far as the kernel is concerned the opened file is a regular file (with some caveats) which generally means the file can be mapped into memory. However, any requests for the contents of that file will not be handled directly, but instead handled by a server over a network protocol.

Ideally we should be able to implement our own server, handle the read or write requests to a file mapping which will allow us to detect or delay the request so that we can exploit any TOCTOU. The following table contains only Microsoft MUP drivers that I identified. The table contains what versions of Windows 10 the driver is supported on and whether it’s something enabled by default.

While MUP was designed for remote file systems there’s no requirement that the file system server is actually remote. SMB, WebDAV and NFS are IP based protocols and can be redirected to localhost. P9 uses a local Unix Socket which can’t be remoted anyway. The terminal services client sends file access requests back to the client system over the RDP protocol. For all these protocols we can implement the server with varying degrees of effort and see if we can detect and delay reads and writes to the file mapping.

I decided to focus only on two, SMB and WebDAV. These were the only two which are enabled by default and are trivially usable. While the Remote Desktop Client is in theory installed by default the RDP server is not normally enabled by default. Also setting up the RDP session is complex and might require valid authentication credentials therefore I decided against it.

Server Message Block

SMB is almost as old as Windows itself, having been introduced in Lan Manager 1.0 back in 1987. The latest SMB version 3.1 protocol only bears a passing resemblance to that original version having shed its NetBIOS roots for a TCP/IP connection. Its lineage does mean it’s the best integrated of any of the network file systems, with the MUP APIs being designed around the needs of SMB.

I decided to do a simple test of the behavior of mapping a file over SMB. This is fairly easy as you can access SMB on the same machine via localhost. I first created a 1GiB file on a local disk, the rationale being if SMB supports caching file data it’s unlikely to read something that large in one go. I then started Wireshark and monitored the loopback interface to capture the SMB traffic as shown below.

I then wrote a quick PowerShell script which will map the file into memory and then reads a few bytes from memory at a few different offsets.

This just reads 4 bytes from offset, 0, 256MiB, 512MiB and 768MiB. Going back to Wireshark I filtered the output to only SMBv2 read requests using the display filter smb2.cmd == 8, and the following four packets can be observed.

This corresponds with the exact memory offsets we accessed in the script although the length is always 32KiB in size, not the 4 we requested. Note, that it’s not the typical Windows memory allocation granularity of 64KiB which you might expect. In my testing I’ve never seen anything other than 32KiB requested.

All the bytes we’ve tested are aligned to the 32KiB block, what if the bytes were not aligned, for example if we accessed 4 bytes from address 512MiB minus 2? Changing the script to add the following allows us to check the behavior:

In Wireshark we see the following read requests.

The accesses are still at 32KiB boundaries, however as the request straddles two blocks the kernel has fetched the preceding 32KiB of data from the file and then the following 32KiB. You might think that all makes sense, however this behavior turned out to be a fluke of testing.

Overview diagram of memory read layout. In the middle is a set of boxes representing the native 4KiB pages being read. All the boxes are contained within a single larger region which is the large page size. Above the boxes are arrows which show that from the base of the 4KiB box a 32KiB read will be made into the file which can satisfy the reads from other 4KiB pages. The final box shows that the last 32KiB of the large page size will always be read as a single page regardless of where in the box the read occurs." style="max-height: 750; max-width: 600;" />

The diagram above shows the structure of how mapped file reads are handled. When an address is read the kernel will request 32KiB from the closest 4KiB page boundary, not the 32KiB boundary. However, there’s then a secondary structure on top based on the supported size of large pages. If the read is anywhere within 32KiB of the end of a large page the read offset is always for the last 32KiB.

For example, on my system the large page size (as queried using the GetLargePageMinimum API) is 2MiB. Therefore if you start at offset 512MiB, between 512 and 514 - 32KiB the kernel will read 32KiB from the offset truncated to the closest 4KiB boundary. Between 514 - 32KiB and 514MiB the read will always request offset 514 - 32KiB so that the 32KiB doesn’t cross the large page boundary.

This allows reads at 4KiB boundaries, however the amount of data read is still 32KiB. This means that once one 4KiB page is accessed the kernel will populate the current page and 7 following pages. Is there any way to only populate a single native page? Based on a comment from Mateusz I tested returning short reads. If the SMB server returns fewer bytes than requested from the read then rather than failing it only populates the pages covered by the read. By returning these short reads we can get trap granularity down to the native page size except for the final 32KiB of a large page. If a read request is shorter than the native page size the rest of the page is zeroed.

What about writing? Let’s change the script again to call WriteBytes rather than ReadBytes, for example:

You will see a write request to the file in Wireshark, similar to the following:

However, if you dig a bit deeper you’ll notice that the write only happens once the file is closed, not in response to the WriteBytes call. This makes sense, there isn’t any easy way to detect when the write happened to force the page to be flushed back to the file system. Even if there was a way flushing to a network server for every write would have a massive performance impact.

All is not lost however, before the memory is safe to write it must be populated with the contents from the file. Therefore if you look before the write you’ll see a corresponding read request for the 32KiB region which encompasses the write location which is synchronous with the read. You can detect a write through its corresponding read but you can’t distinguish read from a write at the protocol level.

All this testing indicates if we have control over the server we can detect memory access to the mapped file. Can we delay the access as well? I wrote a simple SMB server in .NET 5 using the SMBLibrary by Tal Aloni. I implemented the server with a custom filesystem handler and added some code to the read path which delays for 10 seconds when the file offset is greater than 512MiB.

The data returned by the read operation can be arbitrary, you just need to fill in the appropriate byte buffers in the read. To test the access times I wrapped the memory read requests inside a Measure-Command call to time the memory access.

To compare the access time a read request is made to a location 4 bytes below the 512MiB boundary and then at the 512MiB boundary. By making two requests we should be able to see if the results differ per-read. The results were as follows:

The first access for below 512MiB takes around a second, this is because the request still needs to be made to the server and the server is written in .NET which can have a slow startup time for running new code. The second request takes significantly less that 1 second, the memory is now cached locally and so there doesn’t need to be any request.

For the accesses above 512MiB the first request takes around 10 seconds, which correlates with the added delay. The second request takes less than a second because the page is now cached locally. This is exactly what we’d expect, and proves that we can at least delay for 10 seconds. In fact you can delay the request at least 60 seconds before the connection is forcibly reset. This is based on the session timeout for the SMB client. You can query the SMB client timeout using the following command in PowerShell:

A few things to note about the SMB client’s behavior which came out of testing. First the client or the Windows cache manager seem to be able to do some caching of the remote file. If you request a specific access when opening the file, such as GENERIC_READ | GENERIC_WRITE for the desired access then caching is enabled. This means the read requests do not go to the server if they’re previously been cached locally. However if you specify MAXIMUM_ALLOWED for the desired access the caching doesn’t seem to take place. Secondly, sometimes parts of the file will be pre-cached, such as the first and last 32KiB of the file. I’ve not worked out what is the cause, oddly it seems to happen more often with native code than .NET code, so perhaps it’s Windows Defender peeking at memory or perhaps Superfetch. In general as long as you keep your memory accesses somewhere in the middle of a large file you should be safe.

If you’ve run the example code you might notice a problem, running the example server locally fails with the following error:

System.Net.Sockets.SocketException (10013): An attempt was made to access a socket in a way forbidden by its access permissions.

By default Windows 10 has the SMB server enabled. This takes over the TCP ports and makes them exclusive so it’s not possible to bind to them from a normal user. It is possible to disable the local SMB server, but that would require administrator privileges. Still, it was worth verifying whether the SMB server approach will work even if we have to communicate with a remote server.

I did do some investigation into tricks I could use to get the built-in SMB server to work for our purposes. For example I tried to use the fact that you can set an Opportunistic Lock which will trap file reads. I used this trick to exploit a TOCTOU vulnerability in the LUAFV driver. Unfortunately the SMB server detects the file is already in a lock and waits for the OpLock break to occur before allowing access to the file. This made it a non-starter.

For testing you can disable the LanmanServer service and its corresponding drivers. If you wanted to use this on an arbitrary system you'd almost certainly need to connect to a remote server. I’ve released the example server code here, which can be repurposed, although it is only a demonstrator. It allows for read granularity of the native page size, which is assumed to be 4KiB. The server code should work on Linux but as of version 1.4.3 of SMBLibrary on NuGet there’s a bug which causes the server to fail when starting. There is a fix in the github repository but at the time of writing there’s no updated package.

How well does abusing the SMB client meet with our criteria from earlier? I’ve crossed out all the ones we’ve met.

• Works on a default installation of Windows 10 20H2.
• Gives a clear signal when memory is read or written.
• Works when memory is accessed from both user and kernel mode.
• Allows for delaying memory access indefinitely.
• The data in the memory accessed is arbitrary.
• The primitive can be set up from a range of privilege levels.
• Can trap multiple times during the same exploit.

Using the SMB client does meet the majority of our criteria. I verified that it doesn’t matter whether kernel or user mode code accesses the memory it will still trap. The biggest problem is it’s hard to use this from a sandboxed application where it would perhaps be most useful. This is because MUP restricts access to remote file systems by default from restricted and low IL processes and AppContainer sandboxes need specific capabilities which are unlikely to be granted to the majority of applications. That’s not to say it’s completely impossible but it’d be hard to do.

While our trick doesn’t really delay the memory read indefinitely, for our purposes the limit of 60 seconds based on the SMB session timeout is going to be enough for most vulnerabilities. Also once the trap has been activated you can’t force the memory manager to request the same page from the server. I tried playing with memory caching flags and direct IO but at least for files over SMB nothing seemed to work. However, you can specify your own base address when mapping a file so you could map different offsets in the file to the same virtual address by unmapping the original and mapping in a new copy. This would allow you to use the same address multiple times.

WebDAV

As SMB can’t be easily used locally, what about WebDAV? By default TCP port 80 is unused on Windows 10 so we can start our own web server to communicate with. Also unlike on Linux there’s no requirement for having administrator privileges to bind to TCP ports under 1024. Even if either of these were not the case the WebDAV client supports a syntax to specify the TCP port of the server. For example if you use the path \\localhost@8080\share then the WebDAV HTTP connection will be made over port 8080.

However, does the WebDAV client expose the right read and write primitives to allow us to trap on memory access? I wrote a simple WebDAV server using the NWebDav library to serve local files. Running the script but specifying the WebDAV server on port 8080 to open the 1GiB file I’m immediately faced with a problem:

Get-NtFile : (0xC0000904) - The file size exceeds the limit allowed and cannot be saved.

Just opening the file fails with the error code STATUS_FILE_TOO_LARGE. The reason for that can be found in one of many Microsoft Knowledge Base articles such as this one. There’s a default limit of 50MB (that’s decimal megabytes) for any file accessed on a WebDAV share because it used to be possible to cause a denial of service by tricking a Windows system into downloading an arbitrarily large file.

The reason this size limiting behavior is in place is why WebDAV isn’t suitable for this attack. If you resize the file to below 50MB you’ll find the WebDAV client pulls the file in its entirety to the local disk before returning from the file open call. That file is then mapped into memory as a local file. The WebDAV server never receives a GET or PUT request for reads/writes to the memory mapping synchronously so there’s no mechanism to detect or trap specific memory requests.

File System Overlay APIs

Abusing the SMB client does work, but it can’t be used locally on a default installation. I decided I need to look for another approach. As I was looking at Windows Filter Drivers (see last blog post) I noticed a few of the drivers provided a mechanism to overlay another file system on top of an existing one. I trawled through MSDN to find the API documentation to see if anything would be suitable. The three I looked at are shown in the table below.

By far the most interesting one is the Projected File System. This was developed by Microsoft to provide a virtual file system for GIT. It allows placeholder files to be “projected” into a directory on disk and the contents of those files are only “rehydrated” to a full file on demand. In theory this sounds ideal, as long as it would populate the file’s contents piecemeal we could add the delays when receiving the PRJ_GET_FILE_DATA_CB callback.

However a basic implementation based on Microsoft’s ProjectedFileSystem sample code would always rehydrate the entire file during file open, similar to WebDAV. Perhaps there’s an option I missed to stream the contents rather than populate it in one go but I couldn’t find it immediately. In any case the Projected File System is not installed by default making it less useful.

WOF doesn’t really allow you to implement your own file system semantics. Instead it allows you to overlay files from either a secondary Windows Image File (WIM) or compressed on the same volume. This really doesn’t give us the control we’re looking for, you might be able to finagle something to work but it seems a lot of effort.

That leaves us with the Cloud Files API. This is used by OneDrive to provide the local online filesystem but is documented and can be used to implement any file system overlay you like. It works very similar to the Projected File System, with placeholders for files and the concept of hydrating the file on demand. The contents of the files do not need to come from any online service such as OneDrive, it can all be sourced locally. Crucially after some basic testing it supports streaming the contents of the file based on what was being read and you could delay the file data requests and the reading thread would block until the read has been satisfied. This can be enabled by specifying the CF_HYDRATION_POLICY_PRIMARY hydration policy with the value CF_HYDRATION_POLICY_PARTIAL when configuring the base sync root. This allows the Cloud File API to only hydrate the file's parts which were accessed.

This seemed perfect, until I tested with the PowerShell file mapping script where it didn’t work, my cloud file provider would always be requested to provide the entire file. Checking the Cloud Filter driver, when a request is received for mapping a placeholder file, the IRP_MJ_ACQUIRE_FOR_SECTION_SYNCHRONIZATION handler always fully rehydrates the file before completing. If the file is not hydrated fully then the call to NtCreateSection never returns which prevents the file being mapped into memory.

I was going to go back to doing my filter research until I realized I might be able to combine the SMB client loopback with the Cloud Filter API. I already knew that the SMB client doesn’t really map a file, even locally, instead it would read it on-demand via the SMB protocol. And I also knew that the Cloud Filter API would allow streaming of parts of the file on-demand as long as the file wasn’t being mapped into memory. The final setup is shown in the following diagram:

To use the primitive we first setup our own cloud provider by registering the sync root directory using the CfRegisterSyncRoot API configuring it with the partial hydration policy. Then a 1GiB placeholder can be created in the directory using CfCreatePlaceholders. At this point the file does not have any contents on disk. If we now open and map the placeholder file via the SMB loopback client the file will not be rehydrated immediately.

Any memory access into the mapping will cause the SMB client to make a request for a 32KiB block, which will be passed to our user-mode cloud provider, which we can detect and delay as necessary. It goes without saying that the contents of the file can also be arbitrary. Based on testing it doesn’t seem like you can force the read granularity down to the native page size like when implementing a custom SMB server, however you can still make requests at native page size boundaries within the large page size constraint. It might be possible to modify the file size to trick the SMB server into doing short reads but this behavior has not been tested. A sample implementation of the cloud provider is available here.

Usage Examples

We now have an exploitation trick which allows us to trap and delay virtual memory reads and writes. The big question is, does this improve the exploitation of vulnerabilities such as double fetches? The answer depends on the actual vulnerability. A quick note, when I use the word page I’m meaning the unit of memory which will cause a request to the SMB server, e.g. 32KiB not the native page size such as 4KiB.

Let’s take the example given at the start of this blog post. This vulnerability reads the value from the same memory address, lpInputPtr, twice. First for the comparison, then for the size to copy.  The problem for exploitation is one of the limitations of the technique is the memory trap is one shot. Once the trap has fired to read the size for the comparison you can delay it indefinitely. However, once you provide the requested memory page and the faulting thread is resumed it won’t fire on the second read, it’ll just be read from memory as if it was always there.

You might wonder if you could remap the memory page when you detect the first read? Unfortunately this doesn’t work. When the thread is resumed it restarts at the faulting instruction and will perform the read again, therefore what would happen is the following:

As you can tell from the diagram you end up trapped in an infinite loop, as you remap a fresh page which just triggers another page fault ad infinitum. If you don’t perform step ③ then the operation will complete and there is a time window between resuming the thread, reading the now valid memory for the size comparison and the second read. However, in this example the time window is likely to be the order of a couple of instructions so using our exploitation trick isn’t better than the existing probabilistic approaches. That said one advantage is you do know when the read occurs which allows you to target the brute force window more accurately.

This example is the worst case, what if there was more time between the reads? Another example from a the Bochspwn paper is shown below:

The same double fetch behavior is present, however what’s different is the value is passed to another function, in this case ExAllocatePool which allocates kernel memory. Depending of the current memory configuration or how large the allocation requested there might be a significant time delay between ① and ②. Is there any way we can win the race?

Well not that I know of, at least not deterministically. But we can exploit one behavior to try to synchronize the reading and writing threads a little. Recall that in order to write to an unresolved page the contents of the page must first be read from the server. Therefore, to maintain consistency any thread writes to the unresolved page must generate a page fault and wait on the same lock as another thread which is just reading from the page, as shown in the following diagram:

By synchronizing the reading and writing threads you’re giving yourself a reasonable chance of causing a write to happen during the time window for exploitation. This is still a probabilistic approach, it depends on the scheduler. For example, it’s possible that the write thread is woken before the read thread which will cause the pointer to always take the final value. Or the read thread could run to completion before the write thread is ever scheduled to run making the value never change. It’s possible there’s some scheduler magic such as using multiple reader or writer threads or by selecting appropriate priorities which you could exploit to guarantee read and write ordering. I’d be surprised if something is reliable across multiple Windows 10 systems. I’d be very interested in anyone who’s got better ideas on how to improve the reliability of this.

One approach you might be wondering about is unaligned access, say splitting the value across two separate pages. From a microarchitecture perspective it’s likely that the read will be split up into two parts, first touching one page then another. However, remember how the page fault works, it generates an exception which causes a handler to execute in the kernel. At this point any work the instruction has already done will have been retired while the kernel deals with the page fault. When the thread is resumed it will restart the faulting instruction, which will reissue the appropriate micro operations to read from the unaligned address. Unless the compiler generated two loads for the unaligned access (which might happen on some architectures) then there is no way I know of to restart the memory access instruction part of the way through.

This all seems slightly downbeat on the usefulness of the exploitation trick. Thing is, there’s as many different types of vulnerability as there are fish in the sea (if you’re reading this in 2100, I apologize for the acidification of the seas which killed all marine life, choose your own apocalypse-appropriate proverb instead). For example if we modify the original example as follows:

The check now ensures the buffer is large enough and a second DWORD in the buffer is not set to 2. The second field might represent the buffer type, and type 2 isn’t valid for this request. If you check the compiler output for this code, such as on Godbolt, the difference in native code is 2 or 3 instructions. This would seem to not materially improve the odds of winning the TOCTOU race when using a naïve probabilistic approach. But with our exploitation trick we can now build a deterministic exploit.

The diagram above shows how we can achieve this deterministic exploit. We can place the Size field on a different page to the rest of the input buffer, although the buffer is still contiguous in virtual memory. The first page (N-1) should already be faulted into memory and contain the Size field which is smaller than the LocalBuffer size. We can let the read for the size ① complete normally.

Next the code will read the Type field which is on page N ②. This page isn’t currently in memory and so when it’s accessed a page fault will occur ③. This requires the kernel to read the contents from the file, which we can detect and delay. When the read is detected we have as long as we need to modify the Size field to contain a value larger than the LocalBuffer size ④. Finally we complete the read, which will restart the thread back at the Type field read instruction ⑤. The code can continue and will now read the overly large Size field and cause memory corruption.

The key takeaway is that if between the double fetch points the code touches any user mode memory under your control which is not the one being double fetched it should be possible to convert that into a deterministic exploit. It doesn’t matter if the target system only has a single CPU, what the scheduling algorithm is in the kernel, how many instructions are between the double fetch points or what day of the week it is etc, it should “just work”.

The followup blog post on double-fetch exploitation gives some figures for exploitability. The examples shown up to now, when the right timing window is chosen the chance of success can hit 100% after some number of seconds. However, as shown here we can get 100% reliability on some classes of the same bug, but in the best case this isn’t an improvement other than it being deterministic.

All examples up to now only demonste the exploitation of what the blog post refers to as arithmetic races. The blog also mentions a second class of bug, binary races, which are harder to exploit and never reach 100% success. Let’s look at the example in the blog and see if our exploitation trick would do better.

On the face of it this doesn’t look massively different to previous examples, however in this case the destination pointer is being changed rather than the size. The ProbeForWrite kernel API which checks the pointer is both at a user-mode address and the memory is writable. This is a commonly used idiom to verify a user supplied pointer is not pointing into kernel memory.

If the pointer value is changed between ① and ② from a user mode address to a kernel mode address the example would overwrite kernel memory. The behavior is harder to exploit with a probabilistic exploit as there are only two valid values of the pointer, either a user-mode address or a kernel mode address. If you’re brute forcing the pointer value then it’s possible to end up where both fetches read a user-mode pointer even though it might change to a kernel pointer in between the fetches.

Fortunately, due to the call to ProbeForWrite this is trivial to exploit if you can trap on user memory access as shown in the following diagram:

From the diagram the first read from UserPointer is made ① and the resulting pointer value passed to ProbeForWrite. The ProbeForWrite API first checks if the pointer is in the user-mode address space, then probes each page of memory up to the size of the length parameter ②. If the page is invalid or is not writable then an exception will be generated and caught by the example's __except block. This gives us our exploit opportunity, we can use the exploitation trick on the one of the user-mode pages which is being probed which will cause ProbeForWrite to generate a page fault we can trap ③. However as the address being probed is not the same as the one storing the pointer we can modify it to contain a kernel mode address while the request is trapped ④. The result is we can deterministically win the race.

Of course I’ve been focussing on kernel double fetches as it’s what originally drew me to look for this behavior. There are many scenarios where this can be used to aid exploitation of user-mode applications. The most obvious one is where a service is sharing memory with a lower privileged application. An example of this sort of issue was a double-fetch in the DfMarshal COM marshaler. The COM marshaler shared a memory section between processes so it was possible to provide a section which exploited our trick. In the end this trick wasn’t necessary as the logic of the vulnerable code allowed me to create an infinite loop to extend the double fetch window. However if that didn't exist we could use this trick to detect and delay when the code was at the point where the handle could be switched.

Another more subtle use is where a privileged process reads memory from a less privileged process. This might be explicit use of APIs such as ReadProcessMemory or it could be indirect, for example querying for the process’ command line using NtQueryInformationProcess will read out memory locations under our control.

The thing to remember with this exploitation trick is it can be used to open up the window to win a timing race. In this case it’s similar to my previous work on oplocks, but instead for memory access. In fact the access to memory might be incidental to the vulnerable code, it doesn’t have to be a memory double fetch or necessarily even a TOCTOU vulnerability. For example you might be trying to win a race between two file paths with symbolic links. As long as the vulnerable code can be made to probe a user mode address we control then you can use it as a timing signal and to widen the exploitation window.

Conclusions

I’ve described an exploitation trick by combining SMB and the Cloud File API which can aid in demonstrating exploitation of certain types of the application and kernel vulnerabilities. It’s possible that there are other ways of achieving a similar result with APIs I haven’t looked at, but for now this is the best approach I’ve come up with. It allows you to trap on reads from user-mode memory, detect when the access occurs and delay the read for at least 60 seconds. Examples of code to implement the SMB and Cloud File API tricks are available here.

It’s worth just reiterating some more of the limitations of this exploitation trick before we conclude.

• Can’t be used in a sandbox, only from a normal user privilege.
• Only allows a one shot for any page mapped from the file. If something else (such as AV) tries to read that page or from the file then the trap may fire early.
• Can’t detect the exact location of a read, limited to a granularity of 4KiB. For local access via the Cloud File API this will always populate the next 7 pages as well as part of the 32KiB read. If accessing a custom SMB server the read size can be reduced to 4KiB. Would prevent exploitation of certain bugs which require precise trapping only on a small area within a larger structure.
• Can only detect writes indirectly, can’t specifically trap on a write.

From a practical perspective the trick presented here doesn’t significantly improve the win rates for traditional kernel double fetches outlined in the Bochspwn paper. Realistically for most of those classes of vulnerability you’d probably want to use a probabilistic approach, if anything due to its simplicity of implementation. However the trick is applicable to other bug classes where the memory trap is used as a deterministic timing signal adjunct to the vulnerability.

The one shot nature of the trick also makes it of no real benefit to exploiting simple double fetch code paths. Also more complex code which might read and write to a memory address more than once before you get to the vulnerable code which might make managing traps more difficult.

Posted by Natalie Silvanovich, Project Zero

On January 29, 2019, a serious vulnerability was discovered in Group FaceTime which allowed an attacker to call a target and force the call to connect without user interaction from the target, allowing the attacker to listen to the target’s surroundings without their knowledge or consent. The bug was remarkable in both its impact and mechanism. The ability to force a target device to transmit audio to an attacker device without gaining code execution was an unusual and possibly unprecedented impact of a vulnerability. Moreover, the vulnerability was a logic bug in the FaceTime calling state machine that could be exercised using only the user interface of the device. While this bug was soon fixed, the fact that such a serious and easy to reach vulnerability had occurred due to a logic bug in a calling state machine -- an attack scenario I had never seen considered on any platform -- made me wonder whether other state machines had similar vulnerabilities as well. This post describes my investigation into calling state machines of a number of messaging platforms, including Signal, JioChat, Mocha, Google Duo, and Facebook Messenger.

WebRTC and State Machines

The majority of video conferencing applications are implemented using WebRTC, which I’ve discussed in several past blog posts.  WebRTC connections are created by exchanging call set-up information in Session Description Protocol (SDP) between peers, a process which is called signalling. Signalling is not implemented by WebRTC, which allows peers to exchange SDP in whatever secure communication message is available to them, usually WebSockets for web applications, and secure messaging for messaging applications.

There are a few types of SDP that can be exchanged by WebRTC peers. In a typical connection, the caller starts off by sending an SDP offer, and then the callee responds with an SDP answer. These messages contain most information that is needed to transmit and receive media, including codec support, encryption keys and much more. After the offer/answer exchange, peers can send SDP candidates to other peers. Candidates are potential network paths that the two peers can use to connect to each other, and SDP candidates contain information such as IP addresses and TURN servers. Peers usually send more than one candidate to a peer, and candidates can be sent at any time during a connection.

WebRTC connections maintain an internal state related to whether an offer or answer has been received and processed, however, applications that use WebRTC usually have to maintain their own state machine to manage the user state of the application. How the user state maps to the WebRTC state is a design choice made by the WebRTC integrator, which has both security and performance consequences. For example, some applications do not exchange any SDP until the callee user has interacted with the application to answer the call, meanwhile others set up the peer-to-peer connection, and start sending audio and video from caller to callee before the callee is even notified of the call.

Regardless of design, transmitting audio or video from an input device must be directly enabled by application code using WebRTC. This is usually done using a feature called tracks. Every input device is considered a ‘track’, and each specific track must be added to a specific peer connection by calling addTrack (or language equivalent) before audio or video is transmitted. Tracks can also be disabled, which is useful for implementing mute and camera-off features. Each track also has an RTPSender property that can be used to fine-tune the properties of transmission, which can also be used to disable audio or video transmission.

Theoretically, ensuring callee consent before audio or video transmission should be a fairly simple matter of waiting until the user accepts the call before adding any tracks to the peer connection. However, when I looked at real applications they enabled transmission in many different ways. Most of these led to vulnerabilities that allowed calls to be connected without interaction from the callee.

Signal Messenger

I looked at Signal in September 2019, and at that time, the application had a calling setup that is very similar to what is recommended in WebRTC documentation.

A peer-to-peer connection is established, and then the callee's audio track is added to the connection when the callee accepts the call by interacting with the user interface. Then a message is sent to the caller via the peer-to-peer connection, telling it to also move to the connected state and add the track.

Unfortunately, the application didn’t check that the device receiving the connect message was the caller device, so it was possible to send a connect message from the caller device to the callee. This caused the audio call to connect, allowing the caller to hear the callee’s surroundings. I tested this bug by changing Signal’s open-source code to send the message and recompiling the attacking client.

This vulnerability was fixed in the client in September 2019, and since then, Signal’s signalling code has been replaced by the ringrtc project, which uses a more conservative state machine.

This bug was purely in Signal’s code, and was not due to a misunderstanding of WebRTC functionality. The state machine design was largely effective requiring user consent to transmit audio, but a specific check was not implemented.

JioChat and Mocha

I accidentally found two very similar vulnerabilities in JioChat and Mocha messengers in July 2020 while testing whether a WebRTC exploit would work on them. They both had a similar signalling design, which was server-mediated.

The offer and answer are exchanged via the server, and then both the caller and the callee send their candidates to the server. The server then stores them until the callee interacts with their device and accepts the call. Then the peer-to-peer connection is created, and when WebRTC enters into its internal connected state, the track is added, causing audio and video to be transmitted.

This design has a fundamental problem, as candidates can be optionally included in an SDP offer or answer. In that case, the peer-to-peer connection will start immediately, as the only thing preventing the connection in this design is the lack of candidates, which will in turn lead to transmission from input devices. I tested this by using Frida to add candidates to the offers created by each of these applications. I was able to cause JioChat to send audio without user consent, and Mocha to send audio and video. Both of these vulnerabilities were fixed soon after they were filed by filtering SDP on the server.

These issues were caused by a misunderstanding of how WebRTC works coupled with an attempt to improve WebRTC performance with an unusual signalling design. Normally, WebRTC integrators have to decide whether to wait until the callee has answered the call to set up the peer-to-peer connection. Setting the connection up early improves performance and prevents the user from having to wait when they answer a call, but also greatly increases the remote attack surface of WebRTC. These applications tried to improve performance without the security cost with this design, but didn’t consider all the ways that WebRTC can start a peer-to-peer connection.

It is generally not a good idea for integrators to gate audio or video transmission on any WebRTC feature that is not adding or enabling tracks. To start, many WebRTC features are complex, so it is easy to make a mistake that allows audio or video to be transmitted. Also, if the feature that is gated on is not commonly-used or not a security feature, it could be poorly tested or changed in the future.

Duo

I looked at Google Duo in September 2020. Duo’s signalling methodology is somewhat different from a lot of messengers because it supports a feature that allows the callee to preview the caller’s video before answering. So a one-way video stream needs to be set up before the call is answered.

The image above shows the setup of the one-way video stream. Dotted lines represent asynchronous calls made using Java executors. The lack of transmission from callee to caller is enforced by two methods. First, the SDP offer contains the property a=sendonly for video, which causes video to only be transmitted in one direction. Also, when the callee receives the offer from the caller, it adds the video track to the peer connection, but then disables it using the RTPSender property of the track (the audio track is not added or enabled until the user accepts the call).

Neither of these methods effectively prevents video from being transmitted from callee to caller. The SDP property is easy to get around because the caller provides the SDP to the callee, so it can be easily altered. Disabling the video track as soon as the offer is processed should work, except for the asynchronous design. Normally, the setLocalDescription method (which processes the SDP offer) calls the callback onSetSuccess, and then sets up the peer-to-peer connection after the callback has finished. However, if the callback makes another asynchronous call, the guarantee that onSetSuccess finishes before the connection is set up no longer holds, because the setLocalDescription method only waits for the onSetSuccess thread to finish. This creates a race between disabling the video and setting up the connection, so in some situations, the callee could transmit a few video frames to the caller before transmission is disabled.

I tested this by using Frida to alter the SDP sent by the callee, and then I tried many methods to win the race. It turned out to be fairly hard to win, and I spent roughly two weeks trying to figure out how to slow down the video disable call enough to give the connection time to set up. I ended up sending multiple offers and adding candidates to the offers, which decreased the connection time, as the network connection was already established. Then I sent many messages that take a long time to process through the data channel of the peer-to-peer connection to slow down the disabling of the video track. Data messages are processed on the same thread queue as disabling the video track in Duo, so sending data messages filled up the queue that was needed to disable video with many other entries, delaying the track being disabled.

This bug was fixed in December 2020 by removing the asynchronous call from onSetSuccess. While Duo generally designed signalling in a way that is effective in preventing video transmission from callee to caller, implementing the design asynchronously introduced problems. Asynchronous signalling implementations are becoming more common on mobile applications, as there are many unpredictable situations in which WebRTC needs to wait on the network or a peer, and separating function calls into different threads means a delay in one call won’t affect unrelated functionality. However, asynchronous calls make it more difficult to model how a state machine will behave in all situations, so it is important to be cautious about adding asynchronous calls to WebRTC signalling. In this case, the asynchronous call to disable the video track added nothing in terms of performance, as there is no reason any of the calls made to disable the track could block, and onSetSuccess already runs in its own thread and can yield to higher priority threads. It’s important to balance the risk and benefit of asynchronous calls and not indiscriminately include them in an application.

Facebook Messenger

I looked at Facebook Messenger in October 2020. It was a fairly challenging target because of the amount of reverse engineering required. Stepping back a bit, WebRTC has bindings in several programming languages which allow it to be integrated into applications using that language. Most Android applications that integrate WebRTC use the Java bindings. This makes investigating signalling state machines fairly straightforward, as important Java functions, such as setLocalDescription (which processes offers and answers), addRemoteIceCandidate (which processes candidates) and addTrack (which adds tracks to connections) can be hooked in Frida and logged for analysis. It is also reasonably straightforward to change the behavior of the attacker device using these calls.

Facebook Messenger does not use Java bindings to integrate WebRTC, instead it uses C++ bindings. Moreover, it statically links WebRTC to a larger library (librtcR20.so, which is likely the rsys library mentioned in this article), so the symbols for calls to bindings get stripped, making them difficult to hook. In addition, Facebook Messenger serializes SDP into another format before it is transmitted, so it is difficult to determine how signalling works by monitoring traffic.

I eventually realized that the only reasonable way to figure out how Facebook Messenger signalling works was to figure out its network protocol. Thankfully, Facebook has publicly stated that they use fbthrift, a branch of thrift. I loaded the librtcR20.so library into IDA to see if I could find where it called into the thrift library, but while there were a few calls, it looked like the code was mostly statically linked. I eventually figured out that this is because thrift generates serialization code for every protocol implemented, so most of the serialization and deserialization code ends up compiled with the protocol processing code. So I decided to compile fbthrift, make a sample serializer and look at it in IDA, so I could get an impression of what compiled fbthrift serializers look like. I noticed that during serialization, members of an object are serialized by calling a method called writeFieldBegin. I also noticed that when this method is called, the field name is required, even though it is usually not included in the serialized output. So I looked for a function in librtcR20 that was very frequently called with different string parameters that seemed reasonable for field names. Not very many functions fulfilled that criteria, so I was able to identify writeFieldBegin.

At this point, I could find many places where objects are serialized, and needed to identify which one was the message used to set up WebRTC calls.

Earlier, I’d noticed a method in the library called P2PCall::OnP2PMessageFromPeer (note that the symbol for this method is stripped, but the method name is logged when it is called). This seemed a likely place that a deserialized message would be processed. Searching for the string “P2PMessage”, I found the serialization code for a type called P2PMessageRequest. I assumed that this was where call setup messages were created.

Thrift serialization code is generated based on class definitions in a thrift definition file. Based on the field names and types passed to writeFieldBegin, I was able to slowly reverse engineer the complete thrift definition for this type. It was tedious work, because the definition was fairly long, and the code is obfuscated in a way that makes register use inconsistent, so I wasn’t confident that any automated approach would be accurate.

Below is a sample of the serialization code.

Notice that it writes two fields from an object of type Extmap. The first, named id, is a mandatory field. The function that writes the code is as follows.

The field identifier written is 1, and the field type is 8, which translates to i32 (32-bit integer). The second field is an optional field, and the registers to write it are set in the following code.

This sets the field name to uri, the field identifier to 2, and the field type to 8 (also i32). All together, this code can be represented by the following thrift definition.

```

struct Extmap{

        1: i32 id

        2: optional i32 uri

}

```

After similarly reverse engineering every field of the P2PMessageRequest type, I had a complete thrift definition, available here.

I did two things with this thrift definition.  First, I used it to determine the layout of the P2PMessageRequest type in C++. This was extremely valuable, as it allowed me to load the struct definition into IDA with every single field named correctly. This made it much easier to understand how incoming messages are handled in P2PCall::OnP2PMessageFromPeer. This ended up being a bit of a process. fbthrift can generate C++ header files directly from a thrift definition, but these are very long and contain a lot of unnecessary definitions, and can not be processed by IDA. So I ended up compiling the generated source and loading it into IDA, and then exporting the structure definitions and importing them into another IDA instance where librtcR20.so was already loaded. A few fields had different sizes in my compilation versus Facebook’s, but it was close enough that I could get it to work with a few modifications.

Below is an example of code decompiled in IDA with the thrift definition imported, to give an idea of how much easier it makes it to understand the processing of the message object.

I was also able to decode and generate messages sent over the network. To do this, I generated the serialization code from the thrift definition in Python, as thrift supports code generation in many languages. Then, I was able to import this code when using Frida Python to hook functions in Facebook Messenger.

Then I needed to find the code that handled incoming P2PMessageRequest messages. Since these messages are handled by native code, meanwhile most Facebook messages are handled by Java code, I looked for a native call with an appropriate name. I found com.facebook.webrtc.WebrtcEngine.onThriftMessageFromPeer. I hooked this method with Frida, and fed its byte array parameter in the generated deserializer, and it decoded incoming messages.

I found a similar method used to send thrift messages, sendThriftToPeer (this method’s class name is obfuscated and changes in every version of Facebook Messenger, but it can be found by grepping the application’s smali). I was also able to hook this method, and alter its byte array parameter, to change a P2PMessageRequest message sent by Facebook Messenger.

Now, I was able to understand Facebook Messenger’s signalling state machine. There are two different ways that signalling can occur, depending on where the user is signed into Facebook Messenger. If the user is signed in on multiple devices or browsers, very little happens before the callee interacts with their device. The offer, answer and candidates are exchanged, but they are stored by the callee device and not processed until the callee user answers the call. This makes sense, because Facebook Messenger doesn’t know what device to connect to otherwise.

If the callee is only signed in on a single device, the state machine is more interesting.

In this case, Facebook Messenger enables the track as soon as an offer is received, but alters the offer so that all outgoing streams are inactive. It then replaces the offer with one where they are active when the user interacts with the device.

I was concerned that there might be a way to bypass the alteration of the offer, but I looked at how this was done, and while I generally don’t recommend using anything other than adding or disabling tracks to disable input device transmission, it was fairly robust. The offer is altered after the SDP is decoded into an internal WebRTC object, and the changes are made directly to this object, which eliminates the possibility of parsing errors.

However, looking at how incoming messages are handled, I noticed that many message types other than offers, answers and candidates are processed before the call is answered. One type that stood out was called SdpUpdate. When an SdpUpdate message is received, the local offer or answer is updated by calling setLocalDescription.

This message type didn’t do anything when sent to the state machine above, as it is already storing SDP and waiting to call setLocalDescription. But in the situation where the user is logged into two devices, it caused setLocalDescription to be called and started the audio connection.

It is not clear what the SdpUpdate message type is used for in Facebook Messenger. I tried many scenarios on my test devices, including network switchover, and was not able to generate one in normal use. Regardless, it is clear that it was not intended for this message type to be received before the call is answered. It is similar to the Signal bug described above, in that it is not related to the application’s use of WebRTC, but due to a missing check when handling input that can cause state transitions.

This vulnerability was fixed in November 2020 with server changes that prevent this message type from being sent before a call is connected.

Other Applications

There were a few other applications I looked at and did not find problems with their state machines. I looked at Telegram in August 2020, right after video conferencing was added to the application. I did not find any problems, largely because the application does not exchange the offer, answer or candidates until the callee has answered the call. I looked at Viber in November 2020, and did not find any problems with their state machine, though challenges reverse engineering the application made this analysis less rigorous than the other applications I looked at.

Discussion

The majority of calling state machines I investigated had logic vulnerabilities that allowed audio or video content to be transmitted from the callee to the caller without the callee’s consent. This is clearly an area that is often overlooked when securing WebRTC applications.

The majority of the bugs did not appear to be due to developer misunderstanding of WebRTC features. Instead, they were due to errors in how the state machines are implemented. That said, a lack of awareness of these types of issues was likely a factor. It is rare to find WebRTC documentation or tutorials that explicitly discuss the need for user consent when streaming audio or video from a user’s device.

Many of these state machines had needless complexity in how they handled call set-up, which was also a factor. Unnecessary threading, reliance on obscure features and large numbers of states and input types increase the likelihood of this type of vulnerability occurring in a signalling state machine.

It is also concerning to note that I did not look at any group calling features of these applications, and all the vulnerabilities reported were found in peer-to-peer calls. This is an area for future work that could reveal additional problems.

Conclusion

I investigated the signalling state machines of seven video conferencing applications and found five vulnerabilities that could allow a caller device to force a callee device to transmit audio or video data. All these vulnerabilities have since been fixed. It is not clear why this is such a common problem, but a lack of awareness of these types of bugs as well as unnecessary complexity in signalling state machines is likely a factor. Signalling state machines are a concerning and under-investigated attack surface of video conferencing applications, and it is likely that more problems will be found with further research.

Depending on your life experiences, the phrase (or country song by Eric Church) “two pink lines” may bring up a wide range of powerful emotions.    I suspect, like many fathers and expecting fathers, I will never forget the moment I found out my wife was pregnant.  You might recall what you were doing, or where you were and maybe even what you were thinking.   As a professional ethical hacker, I have been told many times – “You just think a little differently about things.”   I sure hope so, since that’s my day job and sure enough this experience wasn’t any different.  My brain immediately asked the question, “How am I going to ensure my family is protected from a wide range of cyberthreats?”   Having a newborn opens the door to all sorts of new technology and I would be a fool not to take advantage of all devices that makes parenting easier.   So how do we do this safely?

The A-B -C ‘s

The security industry has a well-known concept called the “principle of least privilege. “This simply means that you don’t give a piece of technology more permissions or access than it needs to perform its primary function.   This can be applied well beyond just technology that helps parents; however, for me it’s of extra importance when we talk about our kids.  One of the parenting classes I took preparing for our newborn suggested we use a baby tracking phone app.   This was an excellent idea, since I hate keeping track of anything on paper.  So I started looking at a few different apps for my phone and discovered one of them asked for permission to use “location services,” also known as GPS, along with access to my phone contacts.  This caused me to pause and ask: Why does an app to track my baby’s feeding schedule need to know where I am?  Why does it need to know who my friends are?   These are the types of questions parents should consider before just jumping into the hottest new app.  For me, I found a different, less popular app which has the same features, just with a little less access.

It’s not always as easy to just “find something else.”  In my house, “if momma ain’t happy, nobody is happy.”  So, when my wife decided on a specific breast pump that came with Bluetooth and is internet enabled, that’s the one she is going to use.   The app backs up all the usage data to a server in the cloud.   There are many ways that this can be accomplished securely, and it is not necessary a bad feature, but I didn’t feel this device benefited from being internet connected.   Therefore, I simply lowered its privileges by not allowing it internet access in the settings on her phone.  The device works perfectly fine, she can show the doctor the data from her phone, yet we have limited our online exposure and footprint just a little more.  This simple concept of least privilege can be applied almost everywhere and goes a long way to limiting your exposure to cyber threats.

Peek-A-Boo

I think one of the most sought after and used products for new parents is the baby monitor or baby camera.   As someone who has spent a fair amount of time hacking cameras (or cameras on wheels) this was a large area of concern for me.  Most cameras these days are internet connected and if not, you often lose the ability to view the feed on your phone, which is a huge benefit to parents.  So how, as parents, do we navigate this securely?  While there is no silver bullet here, there are a few things to consider.    For starters, there are still many baby cameras on the market that come with their own independent video screen.  They generally use Wi-Fi and are only accessible from home.  If this system works for you, use it.  It is always more secure to have a video system which is not externally accessible.   If you really want to be able to use your phone, consider the below.

• Where is the recorded video and audio data being stored? This may not seem important if the device is internet connected anyway, but it can be.  If your camera data is being stored locally (DVR, SD card, network storage, etc.), then an attacker would need to hack your specific device to obtain this information.   If you combine this with good security hygiene such as a strong password and keeping your device updated, an attacker has to work very hard to access your camera data.  If we look at the alternative where your footage is stored in the cloud, and it becomes subject to a security breach, now your camera’s video content is collateral damage.  Large corporations are specifically targeted by cybercriminals because they provide a high ROI for the time spent on the attack; an individual practicing good cybersecurity hygiene becomes a much more difficult target providing less incentive for the attacker, thus becoming a less likely target.
• Is the camera on the same network as the rest of your home? An often-overlooked security implication to many IoT devices, but especially cameras, is outside of the threat of spying, but rather the threat of a network entry point. If the camera itself is compromised it can be used as a pivot point to attack other devices on your network.  A simple way to reduce this risk is to utilize the “guest” network feature that comes by default on almost all home routers.   These guest networks are preset to be isolated from your main network and generally require little to no setup.  By simply attaching your cameras to your guest network, you can reduce the risk of a compromised camera leading a cybercriminal to the banking info on your laptop.

Background checks – Not only for babysitters

Most parents, especially new ones, like to ensure that anyone that watches their children is thoroughly vetted.  There are a ton of services out there to do this for babysitters and nannies, however it’s not always as easy for vetting the companies that create the devices we put in our homes.  So how do we determine what is safe?  My father used to tell me: “It’s how we respond to our mistakes that makes the difference.”  When researching a company or device, should you find that the device has been found to have a vulnerability, often the response time and accountability from the vendor can tell you if it’s a company you should be investing in. Some things to look for include:

• Was the vulnerability quickly patched?
• Are there unpatched bugs still?
• Has a vendor self-reported flaws, fixed them and reported to the public they have been fixed?
• Are there numerous outstanding bugs filed against a company or device?
• Does the company not recognize the possibility of bugs in their products?

These answers can often be discovered on a company’s website or in release notes, which are generally attached to an update of a piece of software.   Take a minute to read the notes and see if the company is making security updates. You don’t need to understand all the details, just knowing they take security seriously enough to update frequently is important.  This can help tip the scales when deciding between devices or apps.

Remember, you can do this!

Through my preparation for becoming a new parent, I constantly read in books and was told by professionals, “Remember, you can do this!”  Cybersecurity in the context of being a parent is no different.  Every situation is different, and it is important to do what works with you and your family.  As parents, we shouldn’t be afraid to use all the cool new gadgets that are emerging on the market, but instead educate ourselves on how to limit our risk.  Which features do I need, which ones can I do without?   Remember always follow a vendor’s recommendations and best practices, and of course remember to breathe!

The post Two Pink Lines appeared first on McAfee Blog.

As mentioned in the article about CVE-2020-1299 [1], this article I will present some bugs I found in processing the "LNK search" file of windows. In the article I will introduce pe-afl [2], a fuzzer I usually use instead of Winafl when Winafl is unstable on newer versions of windows.

The bugs I present below are not fixed, but this blog I wrote at the suggestion of MSRC, they will prepare every answer with customer questions around these unresolved bugs.

Introduction

I will use pe-afl to fuzz the "LNK search" file format, this is the file format I ignored until the ZDI public blog analyzed the error related to this file format [3], about the file structure "LNK search" is very clear in ZDI's blog (I think it is too complicated, I don't read much about it, lol).

Pe-afl

As I mentioned a lot that Winafl does not work well on new windows versions, plus Winafl using dynamic instruments to calculate coverage will result in overhead, which reduces performance.

Pe-afl is a fuzzer built on the AFL used for binary close sources. It uses static instruments to calculate coverage and feedback-driven. A static instrument is always better than a dynamic instrument. However, currently pe-afl only supports instruments for 32-bit binaries and has some limitations because not every binary can be instrumental (most binaries built with visual studio can be instrumental). This is an introduction slide for pe-afl [4] [5].

About using pe-afl is similar to Winafl, except that Winafl requires Dynamorio to calculate coverage + feedback-driven and pe-afl does not. Pe-afl will use IDA to find basic blocks, which are highlighted as shown in the picture.

Then pe-afl will insert the code at the beginning of these basic blocks to mark coverage. Binary instrumented by pe-afl is similar to afl-gcc instrument when building a program.

For pe-afl we will instrument the DLLs or even the EXE executables we want fuzz (pe-afl also supports fuzz, instrument kernel .sys). Then the fuzzer will do everything from calculating the coverage, feedback-driven like afl.

Windows.storage.search.dll and StructuredQuery.dll

Here I debug with the harness I used to fuzz in the previous article [1], I found the program also loads 2 more DLLs: windows.storage.search.dll and StructuredQuery.dll to parsing a file “LNK search”.

I want to use pe-afl to fuzz both DLLs, not just StructuredQuery.dll alone. However, if we use pe-afl to fuzz, we can only cover one module, so in order to cover more modules, I decided to fix a bit of pe-afl's source.

Pe-afl uses an array of 65536 bytes to store coverage for a module, so I doubled the size of this array to be able to cover another module. This way is quite simple but affects the running speed of fuzzer a lot. I don't know what Dynamorio used to cover multiple modules at a time, but for now, I can only use this simple method. I fixed the maximum pe-afl coverage to 3 modules at a time. In addition, I also integrated afl-fast [6] of @thuanpv_ and afl-mopt [7] (inspired by afl++ [8]).

Above is one of the pieces of code I modify pe-afl. Check how it works after I fix it with afl-showmap:

Now everything is available, but the most important thing is that corpus is not available. Corpus about the "LNK search" file format is not mentioned anywhere on the internet. The only way here is to create it manually. I use the search interface of Explorer, creating enough search cases under conditions such as Date, kind, size, ... but also only create about 200 files (boring work makes me impatient).

I then use afl-cmin to reduce the number of corpus and start fuzz. I use pe-afl fuzz on 2 DLLs that are windows.storage.search.dll and structuredquery.dll, after 1 week I checked the crash that fuzzer found and had 3 unique crashes:

-       Stack overflow (0xc00000fd)

-       2 null pointer dereference

All three crashes appear in structuredquery.dll and can cause explorer.exe to crash.

I reported to Microsoft but they said that these errors only caused DOS temporarily they would not fix. However, in my opinion, due to the nature of the LNK file, the default explorer will automatically handle them. For ordinary users who encounter these cases, it is often not clear the cause to fix (simply delete it but due to a continuous crash it will be very difficult to manipulate).

Conclusion

Above are some errors I found for the "LNK search" file format, I will not publish these DOS POCs, but if someone reads this blog and follow it, it can easily get those POCs, even there may be errors that cause RCE that I cannot find (I think there are still errors that exist in processing LNK files). Windows users should protect themselves, not arbitrarily download file formats such as LNK, maybe your computer will be exploited as soon as this file is saved.

[1] https://blog.vincss.net/2020/06/cve49-microsoft-windows-lnk-remote-code-execution-vuln-cve-2020-1299-eng.html

[2] https://github.com/wmliang/pe-afl

[3] https://www.zerodayinitiative.com/blog/2020/3/25/cve-2020-0729-remote-code-execution-through-lnk-files

[4] https://www.slideshare.net/wmliang/make-static-instrumentation-great-again-high-performance-fuzzing-for-windows-system

[5] https://www.youtube.com/watch?v=OipNF8v2His

[6] https://github.com/mboehme/aflfast

[7] https://github.com/puppet-meteor/MOpt-AFL

[8] https://github.com/AFLplusplus/AFLplusplus

--------------

Vietnamese version

Như đã đề cập ở bài viết về CVE-2020-1299 [1], bài này tôi sẽ trình bày 1 số lỗi tôi tìm thấy trong quá trình xử lý file LNK search của windows. Trong bài viết tôi sẽ giới thiệu pe-afl [2], 1 fuzzer tôi thường sử dụng thay thế winafl khi mà winafl chạy không ổn định trên các phiên bản windows mới hơn.

Những lỗi tôi trình bày dưới đây đều không được fix nhưng blog này tôi viết theo gợi ý của MSRC, họ sẽ chuẩn bị mọi câu trả lời với những câu hỏi của khách hàng quanh những lỗi không được fix này.

Introduction

Tôi sẽ sử dụng pe-afl để fuzz định dạng file LNK search, đây là định dạng file tôi đã bỏ qua mãi cho đến khi ZDI public blog phân tích lỗi liên quan đến định dạng file này [3], về cấu trúc file LNK search trong blog của ZDI nói rất rõ (tôi nhận định rằng nó quá phức tạp, tôi đọc cũng không đọng lại được gì nhiều).

Pe-afl

Như tôi đã đề cập rất nhiều rằng winafl chạy không ổn định trên các windows version mới, cộng thêm việc winafl sử dụng dynamic instrument để tính coverage thì sẽ xảy ra overhead, hiệu năng giảm.

Pe-afl là 1 fuzzer được xây dựng dựa trên afl sử dụng đối với các binary close source. Nó sử dụng static instrument để tính coverage và feedback driven. Tất niên static instrument sẽ luôn luôn tốt hơn dynamic instrument. Tuy nhiên hiện tại pe-afl chỉ hỗ trợ instrument đối với các binary 32 bit và có 1 số hạn chế vì không phải binary nào cũng có thể instrument được (đa số các binary được build với visual studio đều có thể instrument được). Đây là slide giới thiệu về pe-afl [4] [5].

Về cách sử dụng pe-afl cũng gần tương tự như winafl, chỉ khác cái là winafl cần có Dynamorio để tính coverage + feeback driven còn pe-afl thì không. Pe-afl sẽ sử dụng IDA để tìm các basic-block, các basic-block được highlight như ở trong hình.

Sau đó pe-afl sẽ chèn những đoạn code tại đầu các basic-block này để đánh dấu coverage. Binary được instrument bởi pe-afl cũng tương tự như afl-gcc instrument khi build 1 chương trình.

Đối với pe-afl ta sẽ instrument những DLL hay thâm chí là những file thực thi exe mà chúng ta muốn fuzz (pe-afl cũng hỗ trợ fuzz, instrument kernel .sys). Sau đó fuzzer sẽ làm mọi việc từ tính toán coverage, feeback driven giống như afl.

Windows.storage.search.dll and StructuredQuery.dll

Ở đây tôi debug với harness tôi sử dụng để fuzz ở bài viết trước [1], tôi nhận thấy chương trình còn load thêm 2 dll là windows.storage.search.dll và StructuredQuery.dll để parsing 1 file LNK search.

Tôi muốn sử dụng pe-afl để fuzz cả 2 DLL trên, chứ không chỉ 1 mình StructuredQuery.dll. Tuy nhiên nếu sử dụng pe-afl để fuzz thì chúng ta chỉ có thể coverage được 1 module, để có thể coverage được nhiều module hơn tôi quyết định sửa 1 chút source của pe-afl.

Pe-afl sử dụng 1 mảng 65536 bytes để lưu lại coverage đối với 1 module, tôi đã tăng kích thước của mảng này lên gấp đôi để có thể coverage thêm 1 module nữa. Cách này khá đơn giản nhưng ảnh hưởng đến tốc độ chạy của fuzzer rất nhiều. Tôi không biết Dynamorio dùng cách gì để có thể coverage nhiều module 1 lúc nhưng hiện tại thì tôi chỉ có thể sử dụng cách đơn giản này. Tôi sửa pe-afl tối đa có thể coverage được 3 module 1 lúc. Ngoài ra tôi còn tích hợp afl-fast [6] của anh @thuanpv_ và afl-mopt [7] (lấy cảm hứng từ afl++ [8]).

Trên đây là 1 trong 1 số những đoạn code tôi modify pe-afl. Kiểm tra nó hoạt động như thế nào sau khi tôi sửa với afl-showmap:

Bây giờ đã có đủ mọi thứ nhưng cái quan trọng nhất là corpus thì không hề có sẵn. Corpus về định dạng file LNK search không được đề cập ở bất cứ đâu trên internet. Cách duy nhất ở đây là chỉ có thể tự tạo bằng tay. Tôi sử dụng giao diện search của explorer, tạo đủ các trường hợp search theo điều kiện như Date, kind, size, … nhưng cũng chỉ tạo được khoảng hơn 200 files (công việc thật nhàn chán khiến tôi thiếu kiên nhẫn)

Sau đó tôi sử dụng afl-cmin để giảm số lượng corpus xuống và bắt đầu fuzz. Tôi sử dụng pe-afl fuzz trên 2 DLL là windows.storage.search.dll và structuredquery.dll, sau 1 tuần tôi kiểm tra đống crash mà fuzzer tìm được và có 3 unique crash:

-       Stack overflow (0xc00000fd)

-       2 null pointer dereference

Cả 3 crash này đều xuất hiện trong structuredquery.dll và có thể gây crash cho explorer.exe

Tôi report cho Microsoft nhưng họ nói rằng những lỗi này chỉ gây ra DOS tạm thời họ sẽ không sửa. Tuy nhiên theo tôi thì do tính chất của file LNK mặc định explorer sẽ luôn tự động xử lý chúng. Với những người dùng bình thường gặp những trường hợp này thường sẽ không rõ nguyên nhân để khắc phục (đơn giản là xóa nó đi nhưng do crash xảy ra liên tục nên sẽ rất khó thao tác).

Conclusion

Trên đây là 1 số lỗi tôi tìm thấy đối với định dạng file LNK search, tôi sẽ không public những POC gây DOS này tuy nhiên nếu ai đó đọc blog này và làm theo đều có thể dễ dàng có được những POC đó, thậm chí có thể sẽ tồn tại những lỗi gây ra RCE mà tôi không tìm thấy (tôi nghĩ vẫn còn những lỗi đó tồn tại trong quá trình xử lý file LNK). Người dùng windows nên tự bảo vệ chính mình, không nên tùy tiện tải về những định dạng file như LNK, có thể máy của bạn sẽ bị khai thác ngay sau khi file này được lưu xuống.

[1] https://blog.vincss.net/2020/06/cve49-microsoft-windows-lnk-remote-code-execution-vuln-cve-2020-1299-eng.html

[2] https://github.com/wmliang/pe-afl

[3] https://www.zerodayinitiative.com/blog/2020/3/25/cve-2020-0729-remote-code-execution-through-lnk-files

[4] https://www.slideshare.net/wmliang/make-static-instrumentation-great-again-high-performance-fuzzing-for-windows-system

[5] https://www.youtube.com/watch?v=OipNF8v2His

[6] https://github.com/mboehme/aflfast

[7] https://github.com/puppet-meteor/MOpt-AFL

[8] https://github.com/AFLplusplus/AFLplusplus

I will return to writing about what I have done in the past year, it has been 2 years since I came back to the blog. This article I will share the fuzzing experience I have learned through the process of using common fuzzer to find bug in non-source products. The environment I learn is Windows, the fuzzer I usually use is targeted at products on this environment. In this article, I will use a popular fuzzer called Winafl to find errors in popular image viewers such as Irfanview [1], Fast Stone [2], Xnview [3], etc.

Introduction

I will not elaborate on Winafl's architecture, nor how to use it. I will leave some links [4] [5] mentioning these issues at the end of the article, anyone interested can read it.

Why did I choose Image Viewers to approach fuzz? We can say file format fuzzing is a fuzzing direction is almost the most popular today. It takes little time to prepare, accessible, easy to find corpus (depending on the case),... and maybe logic parsing these file formats still have very high errors.

Here I will take an example of Irfanview, how I approach and use fuzzer to find errors parsing Irfanview image formats.

Reverse and understand

We can see Irfanview handles many image file formats. Some formats will be handled in the i_view32.exe program, some other file formats will be handled through plugins deployed through DLLs.

We need to understand how the flow of image files is pushed in. The reverse process to understand that logic is quick or slow, difficult, or easy depending on the complexity of each program. With complex programs, it will take a lot of time.

However in this case I will use DynamoRIO [6] (a tool used to calculate the coverage of the program when it executes) to support my reverse faster.

Using DynamoRIO with IDA's lighthouse plugin [7], we can tell with input how the program will go, what code to execute. Save a lot of time when reversing.

For example, when Irfanview processes a jpeg2000 image format, the command runs drrun.exe to generate file coverage:

drrun.exe -t drcov -- i_view32.exe _00042.j2k

With this command, DynamoRIO will generate a file containing information about the loaded DLLs and coverage of the program and each DLL file.

Here is an example output file coverage:

We can see the JPEG2000.dll DLL is loaded during the processing of _00042.j2k file.

Now use the lighthouse plugin on IDA to see the results. We can see that the commands highlighted in green are the ones that were executed when i_view32.exe processed the _00042.j2k file.

According to the status of the stack at the time the ReadJPG2000_W() function is called, the parameters passed to the function are as follows:

- wchar_t *argv1: the name of the jpeg2000 file needs to be processed.

- int argv2: variable store value 0.

- wchar_t *argv3: a memory of size 2048.

- wchar_t *argv4: a memory of size 2048, initialized by the string "None".

- int argv5, argv6: used to save parameters while parsing jpeg2000 file.

It is very simple, it is possible to build a harness that calls this function and pass the above parameters to parsing a jpeg2000 image file. Because these parameters are completely independent from the program i_view32.exe.

Here is the harness that I wrote:

Great, it works properly with i_view32.exe's jpeg2000 processing stream.

With this harness we will be able to use it for fuzzing. I use corpus on the GitHub repo:

 - openjpeg

 - go-fuzz-corpus

 - and some corpus from previous fuzz projects.

With lighthouse, we can also see coverage of each function in DLL.

Herewith the file I use the coverage of the ReadJPG2000_W function is only about 34%, of course, this figure is not ideal when fuzz, you need to find a corpus to push this number as high as possible.

Use Winafl to fuzz jpeg2000 with the harness I built above:

Looking at the interface Winafl we should be interested in some of the following parameters:

- exec speed: the number of test cases that can be executed on 1s

- stability: this indicator shows stability during fuzzing. When running Winafl there will be a certain number of iterations on that test case, in theory, the iterations on the same test case the coverage value must not be changed. If this value changes, the stability will not be high.

- map density: this parameter shows the coverage of the target when running with the current test case.

These three parameters must be high to be effective when the fuzz is high [4].

For other image file formats, Irfanview is treated the same as jpeg2000. The plugin responsible for parsing image files has the same functions for processing. In addition to fuzz jpeg2000 file format I also tried with other formats such as gif, dicom, djvu, ani, dpx, wbmp, webp, ...

Results: CVE-2019-17241, CVE-2019-17242, CVE-2019-17243, CVE-2019-17244, CVE-2019-17245, CVE-2019-17246, CVE-2019-17247, CVE-2019-17248 , CVE-2019-17249, CVE-2019-17250, CVE-2019-17251, CVE-2019-17252, CVE-2019-17253, CVE-2019-17254, CVE-2019-17255, CVE-2019-17256, CVE -2019-17257, CVE-2019-17258, CVE-2019-17259, CVE-2019-17261, CVE-2019-17262

Tips and Tricks

While using Winafl, I found Winafl to be most stable on Windows 7. Windows 10 is very bad, DynamoRIO has some problems with memory on Windows 10 that leads to fuzzer or crash.

When fuzzing, I recommend turning on the Page Heap for the harness, to better detect out-of-bounds and uninitialized memory errors.

Afl-tmin is a useful tool to help you minimize corpus, which will be very helpful with fuzzer during mutate corpus. However I usually do not use it because it is too slow. I think I will try using the halfempty tool [8] to replace afl-tmin in the future.

Speed ​​up: Harness is used to call the Windows API the less DynamoRIO instrument process faster. In the harness I wrote above, in the main function I use the LoadLibraryA function to load the DLL I need fuzz and my target_offset in the main function, it will greatly reduce the running speed of the fuzzer.

There are many workarounds. There are a few ways that I can read and read [9], changing the offset to start the instrument is quite good, but when using this method I check it when run in debug mode with iterator, my fuzzer is unstable, I Do not know why. Here, I use lief [10] to solve this problem. I will load the library I need to fuzz before executing the main function:

                

                

Speed has improved, but this is not the best way because the speed depends on the target that you fuzz not only depends on the harness you build. I use it because on Windows there is one more fuzzer, but later I prefer to use it instead of Winafl, the way my library loads before the main function matches the architecture of that fuzzer. The following article I will mention more about that fuzzer more.

Corpus: Many people who are new to fuzz will often think of the most important and hardest to build a harness. For me, searching for corpus is the most difficult problem. Searching for corpus with high coverage is very rare, and with these corpus people often will not share because it is very valuable with fuzzing.

When finding a large corpus you should use winafl-cmin to reduce the number of test cases down. There will be test cases whose coverage is duplicated or included in other test cases.

Conclusion

This is the first target I use to learn fuzzing. When my bugs were submitted and got CVE, there were some who said that I farmed and was CVE grass. I also don't argue with those people, I just care what I do, what I will learn from it. Continuing this series on fuzzing, I will share how I approach and fuzz out the bugs of VMWare, Microsoft,... based on what I said in this article.

[3] https://www.xnview.com/en/

[4] https://www.apriorit.com/dev-blog/644-reverse-vulnerabilities-software-no-code-dynamic-fuzzing

[5] https://symeonp.github.io/2017/09/17/fuzzing-winafl.html

[6] https://github.com/DynamoRIO/dynamorio

[7] https://github.com/gaasedelen/lighthouse

[8] https://github.com/googleprojectzero/halfempty

[9] https://github.com/googleprojectzero/winafl/issues/4

[10] https://github.com/lief-project/LIEF