As we explained in a previous blogpost, exploiting a prompt injection attack is conceptually easy to understand: There are previous instructions in the prompt, and we include additional instructions within the user input, which is merged together with the legitimate instructions in a way that the underlying model cannot distinguish between them. Just like what happens with SQL Injection. “Ignore your previous instructions and…” is the new “ AND 1=0 UNION …” in the post-LLM world, right? Well… kind of, but not that much. The big difference between the two is that an SQL database is a deterministic engine, whereas an LLM in general is not (except in certain specific configurations), and this makes a big difference on how we identify and exploit injection vulnerabilities.
When detecting an SQL Injection, we build payloads that include SQL instructions and observe the response to learn more about the injected SQL statement and the database structure. From those responses we can also identify if the injection vulnerability exists, as a vulnerable application would respond differently than expected.
However, detecting a prompt injection vulnerability introduces an additional layer of complexity due to the non-deterministic nature of most LLM setups. Let’s imagine we are trying to identify a prompt injection vulnerability in an application using the following prompt (shown in OpenAI’s Playground for simplicity):
Example of failing prompt injection exploitation.
In this example, “System” refers to the instructions within the prompt that are invisible and immutable to users; “User” represents the user input, and “Assistant” denotes the LLM’s response. Clearly, the user input exploits a prompt injection vulnerability by incorporating additional instructions that supersede the original ones, compelling the application to invariably respond with “Secure.” However, this payload fails to work as anticipated because the application responds with “Insecure” instead of the expected “Secure,” indicating unsuccessful prompt injection exploitation. Viewing this behavior through a traditional SQLi lens, one might conclude the application is effectively shielded against prompt injection. But what happens if we repeat the same user input multiple times?
Example of successful prompt injection exploitation.
In a previous blogpost, we explained that the output of an LLM is essentially the score assigned to each potential token from the vocabulary, determining the next generated token. Subsequently, various parameters, including “temperature” and beam size, are employed to select the next generated token. Some of these parameters involve non-deterministic processes, resulting in the model not always producing the same output for the same input.
Slide of a presentation showing how the next character is chosen under the hood.
This non-deterministic behavior influences how a model responds to inputs that include a prompt injection payload, as illustrated in the example above. Similar behavior might be observed if you have experimented with LLM CTFs, wherein a payload effective for a friend does not appear to work for you. It is likely not a case of your friend cheating; instead, they might just be luckier. Repeating the payload several times might eventually lead to success.
Another factor where the exploitation of prompt injection differs significantly from SQLi exploitation is that of LLM hallucinations. It is not uncommon for a response from an LLM to include a hallucination that may deceive one into believing an injection was successful or had more of an impact than it actually did. Examples include receiving an invented list of previous instructions or expanding on something that the attacker suggested but does not actually exist.
Consequently, identifying prompt injection vulnerabilities should involve repeating the same payloads or minor variations thereof multiple times, followed by verifying the success of any attempt. Therefore, it is crucial to consult with your security vendor about the maximum number of connections they can utilize and how the model is configured to yield deterministic responses. The less deterministic the model and the fewer connections the target service permits, the more time will be needed to achieve comprehensive coverage. If the prompt template and instructions are available, it aids in pinpointing hallucinations and other similar behaviors, which lead to false positives.
Acknowledgements
Special thanks to Thomas Atkinson and the rest of the NCC Group team that proofread this blogpost before being published.
During the spring of 2024, Google engaged NCC Group to conduct a design review of Confidential Mode for Hyperdisk (CHD) architecture in order to analyze how the Data Encryption Key (DEK) that encrypts data-at-rest is protected. The project was 10 person days and the goal is to validate that the following two properties are enforced:
The DEK is not available in an unencrypted form in CHD infrastructure.
It is not possible to persist and/or extract an unencrypted DEK from the secure hardware-protected enclaves.
The two secure hardware-backed enclaves where the DEK is allowed to exist in plaintext are:
Key Management SystemHSM – during CHD creation (DEK is generated and exported wrapped) and DEK Installation (DEK is imported and unwrapped)
Infrastructure Node AMD SEV-ES Secure Enclave – during CHD access to storage node (DEK is used to process the data read/write operations)
NCC Group evaluated Confidential Mode for Hyperdisk – specifically, the secure handling of Data Encryption Keys across all disk operations including:
disk provisioning
mounting
data read/write operations
The public report for this review may be downloaded below:
This blog is part of a series written by various Dutch cyber security firms that have collaborated on the Cactus ransomware group, which exploits Qlik Sense servers for initial access. To view all of them please check the central blog by Dutch special interest group Cyberveilig Nederland [1]
The effectiveness of the public-private partnership called Melissa [2] is increasingly evident. The Melissa partnership, which includes Fox-IT, has identified overlap in a specific ransomware tactic. Multiple partners, sharing information from incident response engagements for their clients, found that the Cactus ransomware group uses a particular method for initial access. Following that discovery, NCC Group’s Fox-IT developed a fingerprinting technique to identify which systems around the world are vulnerable to this method of initial access or, even more critically, are already compromised.
Qlik Sense vulnerabilities
Qlik Sense, a popular data visualisation and business intelligence tool, has recently become a focal point in cybersecurity discussions. This tool, designed to aid businesses in data analysis, has been identified as a key entry point for cyberattacks by the Cactus ransomware group.
The Cactus ransomware campaign
Since November 2023, the Cactus ransomware group has been actively targeting vulnerable Qlik Sense servers. These attacks are not just about exploiting software vulnerabilities; they also involve a psychological component where Cactus misleads its victims with fabricated stories about the breach. This likely is part of their strategy to obscure their actual method of entry, thus complicating mitigation and response efforts for the affected organizations.
For those looking for in-depth coverage of these exploits, the Arctic Wolf blog [3] provides detailed insights into the specific vulnerabilities being exploited, notably CVE-2023-41266, CVE-2023-41265 also known as ZeroQlik, and potentially CVE-2023-48365 also known as DoubleQlik.
Threat statistics and collaborative action
The scope of this threat is significant. In total, we identified 5205 Qlik Sense servers, 3143 servers seem to be vulnerable to the exploits used by the Cactus group. This is based on the initial scan on 17 April 2024. Closer to home in the Netherlands, we’ve identified 241 vulnerable systems, fortunately most don’t seem to have been compromised. However, 6 Dutch systems weren’t so lucky and have already fallen victim to the Cactus group. It’s crucial to understand that “already compromised” can mean that either the ransomware has been deployed and the initial access artifacts left behind were not removed, or the system remains compromised and is potentially poised for a future ransomware attack.
Since 17 April 2024, the DIVD (Dutch Institute for Vulnerability Disclosure) and the governmental bodies NCSC (Nationaal Cyber Security Centrum) and DTC (Digital Trust Center) have teamed up to globally inform (potential) victims of cyberattacks resembling those from the Cactus ransomware group. This collaborative effort has enabled them to reach out to affected organisations worldwide, sharing crucial information to help prevent further damage where possible.
Identifying vulnerable Qlik Sense servers
Expanding on Praetorian’s thorough vulnerability research on the ZeroQlik and DoubleQlik vulnerabilities [4,5], we found a method to identify the version of a Qlik Sense server by retrieving a file called product-info.json from the server. While we acknowledge the existence of Nuclei templates for the vulnerability checks, using the server version allows for a more reliable evaluation of potential vulnerability status, e.g. whether it’s patched or end of support.
This JSON file contains the release label and version numbers by which we can identify the exact version that this Qlik Sense server is running.
Figure 1: Qlik Sense product-info.json file containing version information
Keep in mind that although Qlik Sense servers are assigned version numbers, the vendor typically refers to advisories and updates by their release label, such as “February 2022 Patch 3”.
The following cURL command can be used to retrieve the product-info.json file from a Qlik server:
Note that we specify ?.ttf at the end of the URL to let the Qlik proxy server think that we are requesting a .ttf file, as font files can be accessed unauthenticated. Also, we set the Host header to localhost or else the server will return 400 - Bad Request - Qlik Sense, with the message The http request header is incorrect.
Retrieving this file with the ?.ttf extension trick has been fixed in the patch that addresses CVE-2023-48365 and you will always get a 302 Authenticate at this location response:
> GET /resources/autogenerated/product-info.json?.ttf HTTP/1.1
> Host: localhost
> Accept: */*
>
< HTTP/1.1 302 Authenticate at this location
< Cache-Control: no-cache, no-store, must-revalidate
< Location: https://localhost/internal_forms_authentication/?targetId=2aa7575d-3234-4980-956c-2c6929c57b71
< Content-Length: 0
<
Nevertheless, this is still a good way to determine the state of a Qlik instance, because if it redirects using 302 Authenticate at this location it is likely that the server is not vulnerable to CVE-2023-48365.
An example response from a vulnerable server would return the JSON file:
We utilised Censys and Google BigQuery [6] to compile a list of potential Qlik Sense servers accessible on the internet and conducted a version scan against them. Subsequently, we extracted the Qlik release label from the JSON response to assess vulnerability to CVE-2023-48365.
Our vulnerability assessment for DoubleQlik / CVE-2023-48365 operated on the following criteria:
The release label corresponds to vulnerability statuses outlined in the original ZeroQlik and DoubleQlik vendor advisories [7,8].
The release label is designated as End of Support (EOS) by the vendor [9], such as “February 2019 Patch 5”.
We consider a server non-vulnerable if:
The release label date is post-November 2023, as the advisory states that “November 2023” is not affected.
The server responded with HTTP/1.1 302 Authenticate at this location.
Any other responses were disregarded as invalid Qlik server instances.
As of 17 April 2024, and as stated in the introduction of this blog, we have detected 5205 Qlik Servers on the Internet. Among them, 3143 servers are still at risk of DoubleQlik, indicating that 60% of all Qlik Servers online remain vulnerable.
Figure 2: Qlik Sense patch status for DoubleQlik CVE-2023-48365
The majority of vulnerable Qlik servers reside in the United States (396), trailed by Italy (280), Brazil (244), the Netherlands (241), and Germany (175).
Figure 3: Top 20 countries with servers vulnerable to DoubleQlik CVE-2023-48365
Identifying compromised Qlik Sense servers
Based on insights gathered from the Arctic Wolf blog and our own incident response engagements where the Cactus ransomware was observed, it’s evident that the Cactus ransomware group continues to redirect the output of executed commands to a True Type font file named qle.ttf, likely abbreviated for “qlik exploit”.
Below are a few examples of executed commands and their output redirection by the Cactus ransomware group:
In addition to the qle.ttf file, we have also observed instances where qle.woff was used:
Figure 4: Directory listing with exploitation artefacts left by Cactus ransomware group
It’s important to note that these font files are not part of a default Qlik Sense server installation.
We discovered that files with a font file extension such as .ttf and .woff can be accessed without any authentication, regardless of whether the server is patched. This likely explains why the Cactus ransomware group opted to store command output in font files within the fonts directory, which in turn, also serves as a useful indicator of compromise.
Our scan for both font files, found a total of 122 servers with the indicator of compromise. The United States ranked highest in exploited servers with 49 online instances carrying the indicator of compromise, followed by Spain (13), Italy (11), the United Kingdom (8), Germany (7), and then Ireland and the Netherlands (6).
Figure 5: Top 20 countries with known compromised Qlik Sense servers
Out of the 122 compromised servers, 46 were not vulnerable anymore.
When the indicator of compromise artefact is present on a remote Qlik Sense server, it can imply various scenarios. Firstly, it may suggest that remote code execution was carried out on the server, followed by subsequent patching to address the vulnerability (if the server is not vulnerable anymore). Alternatively, its presence could signify a leftover artefact from a previous security incident or unauthorised access.
While the root cause for the presence of these files is hard to determine from the outside it still is a reliable indicator of compromise.
Responsible disclosure by the DIVD We shared our fingerprints and scan data with the Dutch Institute of Vulnerability Disclosure (DIVD), who then proceeded to issue responsible disclosure notifications to the administrators of the Qlik Sense servers.
Call to action
Ensure the security of your Qlik Sense installations by checking your current version. If your software is still supported, apply the latest patches immediately. For systems that are at the end of support, consider upgrading or replacing them to maintain robust security.
Additionally, to enhance your defences, it’s recommended to avoid exposing these services to the entire internet. Implement IP whitelisting if public access is necessary, or better yet, make them accessible only through secure remote working solutions.
If you discover you’ve been running a vulnerable version, it’s crucial to contact your (external) security experts for a thorough check-up to confirm that no breaches have occurred. Taking these steps will help safeguard your data and infrastructure from potential threats.
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