CVE-2024-12580
Librechat ≤ 0.7.6
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:NSummary
CVE-2024-12580 is a medium-severity Improper Output Neutralization for Logs (CWE-117) vulnerability in Librechat Librechat. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Indicator Removal (T1070); ranked at the 38th 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 Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7005
Vulnerability Data
A vulnerability in danny-avila/librechat prior to version 0.7.6 allows for logs debug injection. The parameters sessionId, fileId, userId, and file_id in the /code/download/:sessionId/:fileId and /download/:userId/:file_id APIs are not validated or filtered, leading to potential log injection attacks. This can cause…
more
distortion of monitoring and investigation information, evade detection from security systems, and create difficulties in maintenance and operation.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: librechat
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect output neutralization when log messages are constructed from untrusted input.
Input validation reduces the chance that specially crafted data reaches log-message construction routines.
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 and coding standards directly require output sanitization for logs.
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 can detect log injection flaws but does not prevent them at the source.
Logging control directly requires proper log generation and handling, which mitigates improper output neutralization.
Monitoring activities rely on trustworthy logs but do not ensure log message integrity.
Secure SDLC includes coding standards that reduce log-related weaknesses but does not specifically address logging.
Secure coding practices mandate input validation and output encoding, directly preventing log injection.