Chainlit AI Framework Vulnerabilities Enable Data Theft and SSRF Attacks

ChainLeak: Vulnerabilities in Chainlit AI Framework

Security researchers at Zafran Security discovered critical vulnerabilities in the Chainlit AI framework, dubbed “ChainLeak,” potentially allowing attackers to steal sensitive data and perform server-side request forgery (SSRF) attacks. Chainlit, a framework for building conversational chatbots, has been downloaded over 7.3 million times, making these flaws a significant concern.

Why This Matters

AI frameworks often inherit the same security pitfalls as traditional software, but the consequences can be amplified due to the sensitive data they process and the critical applications they power. The ChainLeak vulnerabilities demonstrate how seemingly contained flaws in AI infrastructure can quickly escalate into full system compromise, potentially leading to data breaches and significant financial losses for organizations relying on these frameworks.

Key Insights

• CVE-2026-22218 (CVSS 7.1): Arbitrary file read vulnerability due to lack of input validation.
• CVE-2026-22219 (CVSS 8.3): SSRF vulnerability exploitable via the SQLAlchemy data layer backend.
• Chainlit adoption: Over 220,000 downloads in the last week, highlighting widespread use and potential impact.

Working Example

(No code example available in the context)

Practical Applications

• Use Case: A financial institution using Chainlit for a customer support chatbot could have API keys leaked, allowing unauthorized access to customer accounts.
• Pitfall: Relying on default configurations without thorough security reviews, especially when using cloud environments with potentially vulnerable metadata services like IMDSv1.

References:

• https://thehackernews.com/2026/01/chainlit-ai-framework-flaws-enable-data.html