Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:NSummary
CVE-2026-31943 is a high-severity SSRF (CWE-918) vulnerability in Librechat Librechat. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 12th 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 Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-31943 is a server-side request forgery (SSRF) vulnerability in LibreChat, an open-source ChatGPT clone with additional features. The issue affects versions prior to 0.8.3 and stems from a flaw in the `isPrivateIP()` function located in `packages/api/src/auth/domain.ts`. This function fails to properly detect IPv4-mapped IPv6 addresses when presented in their hex-normalized form, enabling bypass of SSRF protections that are intended to block requests to private IP ranges.
Any authenticated user can exploit this vulnerability remotely with low complexity and no user interaction required. Successful exploitation allows the attacker to force the LibreChat server to issue HTTP requests to internal network resources, such as cloud metadata endpoints (e.g., AWS's 169.254.169.254), loopback addresses, and RFC1918 private ranges. The CVSS v3.1 base score of 8.5 (AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:N) reflects high confidentiality impact due to potential exposure of sensitive internal data, with changed scope amplifying the risk.
The GitHub security advisory (GHSA-w5r7-4f94-vp4c) confirms that upgrading to LibreChat version 0.8.3 resolves the issue by addressing the detection flaw in `isPrivateIP()`. Security practitioners should prioritize patching affected instances, review access controls for authenticated users, and monitor for anomalous outbound requests to private IPs as interim mitigations.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16764
Vulnerability Data
LibreChat is a ChatGPT clone with additional features. Prior to version 0.8.3, `isPrivateIP()` in `packages/api/src/auth/domain.ts` fails to detect IPv4-mapped IPv6 addresses in their hex-normalized form, allowing any authenticated user to bypass SSRF protection and make the server issue HTTP requests…
more
to internal network resources — including cloud metadata services (e.g., AWS `169.254.169.254`), loopback, and RFC1918 ranges. Version 0.8.3 fixes the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: chatgpt, librechat
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.