Cyber Resilience

CVE-2026-54033

SSRF in Librechat ≤ 0.8.3

Public PoCSSRF
Published
25 June 2026
Modified
29 June 2026
Patch / advisory
CVSS Score v3.1 7.7
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N
EPSS Score 0.0025 16th percentile
Risk Priority 53 floored blend · peak EPSS

Summary

CVE-2026-54033 is a high-severity SSRF (CWE-918) vulnerability in Librechat Librechat. Its CVSS base score is 7.7 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 16th 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.

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.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

LibreChat is an enhanced ChatGPT clone that supports multiple AI providers. Prior to 0.8.4-rc1, LibreChat allows users to configure custom OpenAI-compatible API endpoints by setting a baseURL. This URL is used to construct HTTP requests without any SSRF validation —…

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no private IP check, no scheme restriction, no DNS pinning. An authenticated user can set baseURL to internal network addresses. This vulnerability is fixed in 0.8.4-rc1.

CWE(s)

AI Security AnalysisAI

AI Category
LLM Application Platforms
Risk Domain
N/A
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: ai, openai

Related Threats

MITRE ATT&CK Enterprise Techniques

T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2026-31945Same product: Librechat Librechat
CVE-2025-69222Same product: Librechat Librechat
CVE-2026-31943Same product: Librechat Librechat
CVE-2024-10366Same product: Librechat Librechat
CVE-2024-11167Same product: Librechat Librechat
CVE-2026-34371Same product: Librechat Librechat
CVE-2024-10361Same product: Librechat Librechat
CVE-2025-66201Same product: Librechat Librechat
CVE-2026-31944Same product: Librechat Librechat
CVE-2026-54036Same product: Librechat Librechat

Affected Assets

librechat
librechat
≤ 0.8.3

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)

Validate
Prove the fix (OWASP ASVS)
  • V1.3.6
  • V1.5.3
  • V5.3.2
  • V10.4.7

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.

PR.PS-06 mostly match
prevents

Secure development practices directly include input validation and destination allow-listing that prevent SSRF.

DE.CM-09 partial match
prevents

Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.

ID.RA-01 partial match
prevents

Vulnerability identification processes can discover and record SSRF flaws in web applications.

PR.IR-01 partial match
prevents

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.

prevents

Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.

References