Ethical Hacking News
A recent discovery has exposed critical vulnerabilities in the popular open-source artificial intelligence (AI) framework Chainlit. These ChainLeak vulnerabilities can potentially enable attackers to steal sensitive data, execute SSRF attacks, and breach organizations' most sensitive secrets. The vulnerabilities were identified by Zafran Security, which has urged organizations to patch the issues with the latest release of Chainlit version 2.9.4. This highlights the importance of timely updates and security testing for AI frameworks in preventing data breaches and maintaining digital asset integrity.
Chainlit, an open-source AI framework, has been found to have two critical vulnerabilities (CVE-2026-22218 and CVE-2026-22219) that can allow attackers to steal sensitive data and execute SSRF attacks. The vulnerabilities were discovered by Zafran Security researchers Gal Zaban and Ido Shani through a responsible disclosure notice. These vulnerabilities can be combined to enable data theft, privilege escalation, and lateral movement within susceptible organizations. A patch for the vulnerabilities has been released in version 2.9.4 of Chainlit, addressing both issues. Another critical vulnerability has been discovered in Microsoft's MarkItDown Model Context Protocol (MCP) server dubbed MCP fURI, which enables arbitrary calling of URI resources and potentially leads to privilege escalation and data leakage.
The cybersecurity landscape has witnessed numerous high-profile breaches in recent years, as malicious actors continue to exploit vulnerabilities in various software frameworks. One such framework that has recently caught the attention of security researchers is Chainlit, an open-source artificial intelligence (AI) framework designed for creating conversational chatbots. A recent discovery by Zafran Security has revealed two critical vulnerabilities in Chainlit, collectively dubbed ChainLeak, which can potentially allow attackers to steal sensitive data and execute SSRF attacks against servers hosting AI applications.
Zafran Security researchers, Gal Zaban and Ido Shani, have exposed the high-severity flaws, highlighting the potential for attackers to leak cloud environment API keys and steal sensitive files or perform server-side request forgery (SSRF) attacks. According to the researchers, these vulnerabilities can be combined in various ways to enable data theft, privilege escalation, and lateral movement within susceptible organizations.
The first vulnerability, CVE-2026-22218, is an arbitrary file read vulnerability in the "/project/element" update flow that allows authenticated attackers to access sensitive files by exploiting a lack of validation for user-controller fields. This vulnerability can be exploited to glean valuable information such as API keys, credentials, and internal file paths that could potentially be used to breach deeper into compromised networks.
The second vulnerability, CVE-2026-22219, is an SSRF vulnerability in the "/project/element" update flow when configured with the SQLAlchemy data layer backend. This vulnerability enables attackers to make arbitrary HTTP requests against internal network services or cloud metadata endpoints from the Chainlit server and store the retrieved responses. The researchers have noted that these vulnerabilities can be combined to enable more sophisticated attacks, including lateral movement within compromised networks.
Zafran's analysis highlights a critical issue with AI frameworks like Chainlit, which are rapidly being adopted by organizations but often introduce new attack surfaces due to their complexity and poorly understood architecture. This is particularly concerning given the increasing use of cloud environments and third-party components in modern software development.
In response to these vulnerabilities, Chainlit has released version 2.9.4, which addresses both issues through a patch released on December 24, 2025. The release follows Zafran's responsible disclosure notice for the vulnerabilities, highlighting the importance of timely patches and updates to mitigate the risk posed by such flaws.
Moreover, BlueRock has disclosed another critical vulnerability in Microsoft's MarkItDown Model Context Protocol (MCP) server dubbed MCP fURI that enables arbitrary calling of URI resources. This exposure allows attackers to execute arbitrary HTTP requests against servers hosting AI applications, potentially leading to privilege escalation, data leakage, and SSRF attacks.
The BlueRock analysis indicates that over 36.7% of more than 7,000 MCP servers are likely exposed to similar SSRF vulnerabilities. To mitigate this risk, it is advised to secure against SSRF attacks by using IMDSv2, implement private IP blocking, restrict access to metadata services, and create an allowlist to prevent data exfiltration.
The discovery of these critical vulnerabilities underscores the need for organizations to prioritize security testing and updates for AI frameworks like Chainlit. It also highlights the importance of timely patching and securing against known vulnerabilities to prevent the breach of sensitive data and maintain the confidentiality and integrity of digital assets.
In conclusion, the recent discovery of the ChainLeak vulnerabilities in Chainlit serves as a warning to organizations adopting AI frameworks, highlighting the need for robust security testing, vulnerability assessment, and timely patching. As organizations move forward with integrating AI into their operations, it is essential that they prioritize the development and implementation of secure by design practices to protect against emerging threats like these.
Related Information:
https://www.ethicalhackingnews.com/articles/Chainlit-AI-Framework-Flaws-Exposed-A-Vulnerability-that-Can-Enable-Data-Theft-via-File-Read-and-SSRF-Bugs-ehn.shtml
https://thehackernews.com/2026/01/chainlit-ai-framework-flaws-enable-data.html
https://nvd.nist.gov/vuln/detail/CVE-2026-22218
https://www.cvedetails.com/cve/CVE-2026-22218/
https://nvd.nist.gov/vuln/detail/CVE-2026-22219
https://www.cvedetails.com/cve/CVE-2026-22219/
Published: Wed Jan 21 04:10:05 2026 by llama3.2 3B Q4_K_M
