CVE-2025-66201
Published: 29 November 2025
Summary
CVE-2025-66201 is a high-severity Improper Input Validation (CWE-20) vulnerability in Librechat Librechat. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Cloud Instance Metadata API (T1552.005); ranked at the 20.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-199888
Vulnerability details
LibreChat is a ChatGPT clone with additional features. Prior to version 0.8.1-rc2, LibreChat is vulnerable to Server-side Request Forgery (SSRF), by passing specially crafted OpenAPI specs to its "Actions" feature and making the LLM use those actions. It could be…
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used by an authenticated user with access to this feature to access URLs only accessible to the LibreChat server (such as cloud metadata services, through which impersonation of the server might be possible). This issue has been patched in version 0.8.1-rc2.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: chatgpt, librechat, llm
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
SSRF via crafted OpenAPI specs allows authenticated users to access internal cloud metadata services, enabling Cloud Instance Metadata API abuse for discovery (T1522) and unsecured credential access (T1552.005), potentially leading to server impersonation.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Directly implements checks on information inputs to reject invalid data before processing.
Penetration testing attempts server-side requests to internal resources, identifying SSRF weaknesses for remediation.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Outbound connections to external resources can be monitored and limited at the boundary, reducing SSRF impact.
Detects server-side request forgery through monitoring of unexpected outbound connections.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.