CVE-2025-8850
Librechat 0.7.9
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-8850 is a high-severity Expected Behavior Violation (CWE-440) vulnerability in Librechat Librechat. Its CVSS base score is 8.8 (High).
Operationally, ranked at the 34th 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 Not Applicable risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-6 (Security and Privacy Function Verification) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-37197
Vulnerability Data
In danny-avila/librechat version 0.7.9, there is an insecure API design issue in the 2-Factor Authentication (2FA) flow. The system allows users to disable 2FA without requiring a valid OTP or backup code, bypassing the intended verification process. This vulnerability occurs…
more
because the backend does not properly validate the OTP or backup code when the API endpoint '/api/auth/2fa/disable' is directly accessed. This flaw can be exploited by authenticated users to weaken the security of their own accounts, although it does not lead to full account compromise.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Not Applicable
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: librechat
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation directly checks whether implemented functions match their specifications.
Security function verification confirms that functions operate according to their defined expected behavior.
Requiring a documented security architecture and design reduces the chance that implementation deviates from intended behavior.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly enforce specification compliance and catch expected-behavior violations during development.
Security testing and exercises help discover behavior deviations before deployment.
Vulnerability identification can surface spec-violating flaws, while eliminating the weakness reduces some vulnerability backlog.
Routine software maintenance and patching can remediate discovered specification violations.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development and acceptance validates that functions behave as specified.
Secure development life cycle mandates verification against specifications, directly reducing expected-behavior violations.
Application security requirements explicitly define expected behavior that must be met.
Secure coding practices enforce adherence to functional specifications during implementation.
Change management can catch specification deviations introduced by modifications.
Documented operating procedures reduce the chance that functions deviate from intended behavior.