Chainlit is an open-source AI framework widely used for building conversational chatbots, with over 7.3 million downloads to date and rapid recent adoption. Security researchers from Zafran Security uncovered two critical vulnerabilities, dubbed ChainLeak, affecting the "/project/element" update flow. The first, CVE-2026-22218 (CVSS 7.1), is an arbitrary file read vulnerability that allows authenticated attackers to read any file accessible by the Chainlit service. This flaw arises from insufficient validation of user-controlled fields, enabling attackers to access sensitive files such as "/proc/self/environ" to extract environment variables, API keys, credentials, internal file paths, and even application source code. The second vulnerability, CVE-2026-22219 (CVSS 8.3), is an SSRF vulnerability exploitable when Chainlit is configured with the SQLAlchemy data layer backend. This allows attackers to send arbitrary HTTP requests from the Chainlit server to internal network services or cloud metadata endpoints. On AWS EC2 instances with IMDSv1 enabled, attackers can retrieve instance metadata and role credentials, facilitating lateral movement and privilege escalation within cloud environments. The combination of these vulnerabilities enables attackers to leak sensitive data, escalate privileges, and move laterally inside compromised networks. Chainlit addressed these issues in version 2.9.4 released on December 24, 2025, following responsible disclosure. The vulnerabilities highlight risks inherent in AI frameworks that embed traditional software flaws, expanding attack surfaces in AI-powered systems. The report also references a similar SSRF vulnerability in Microsoft's MarkItDown MCP server, emphasizing the broader threat of SSRF in cloud environments. The vulnerabilities do not require remote unauthenticated access but do require authentication, which may be easier to obtain in some deployments. No known exploits are currently active in the wild, but the potential impact is significant due to the sensitive nature of data accessible via these flaws.

For European organizations, the ChainLeak vulnerabilities pose a substantial risk to confidentiality, integrity, and availability of AI infrastructure and associated cloud environments. Organizations using Chainlit for chatbot or AI application development may have sensitive data exposed, including API keys, credentials, and internal configuration files. This exposure can lead to unauthorized access to cloud resources, enabling attackers to move laterally within networks, escalate privileges, and potentially compromise additional systems. The SSRF vulnerability's ability to access cloud metadata services is particularly concerning for organizations leveraging AWS EC2 instances with IMDSv1, as attackers can retrieve role credentials and gain broader cloud environment control. The impact extends beyond data theft to potential disruption of AI services and loss of trust in AI deployments. Given the rapid adoption of AI frameworks in Europe and increasing cloud migration, these vulnerabilities could affect critical sectors such as finance, healthcare, and government services, where sensitive data and AI-powered automation are prevalent. The lack of public exploits currently reduces immediate risk but does not diminish the urgency for remediation due to the high severity and ease of exploitation once authenticated access is obtained.

1. Immediately upgrade Chainlit to version 2.9.4 or later, which contains patches for both CVE-2026-22218 and CVE-2026-22219. 2. Enforce the use of AWS IMDSv2 on all EC2 instances to mitigate SSRF attacks targeting instance metadata services. 3. Implement strict network segmentation and firewall rules to restrict outbound HTTP requests from AI application servers, limiting SSRF exploitation scope. 4. Apply allowlisting for internal services accessible by AI frameworks to prevent unauthorized access via SSRF. 5. Conduct thorough access control reviews to minimize the number of users with authentication privileges to Chainlit interfaces. 6. Monitor logs for unusual file access patterns and unexpected internal HTTP requests originating from AI application servers. 7. Regularly audit AI framework deployments for outdated versions and known vulnerabilities. 8. Educate development and security teams about the risks of embedding third-party AI components and the importance of secure configuration. 9. Consider implementing runtime application self-protection (RASP) or web application firewalls (WAF) with custom rules to detect and block exploitation attempts. 10. Review and rotate any potentially exposed API keys or credentials following incident response procedures.