AI Sec Watch: A Security Intelligence Platform for AI Systems

Luu, T.J.

MediumVulnerabilityLLM-specific

GHSA-j37q-q7p9-vpwm: LiteLLM: SSO Debug Flow Has Improper Authentication

Source: GitHub Advisory(opens in a new tab)
Identifiers: CVE-2026-12795GHSA-j37q-q7p9-vpwm
Published: June 21, 2026
Record updated: October 10, 2026

View JSON

Affected: litellm <= 1.82.2
Fixed in: No fixed version was stated when the source was last read.
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.8%

Summary

A vulnerability in BerriAI litellm up to version 1.82.2 affects the json.dumps function in litellm/proxy/management_endpoints/ui_sso.py, part of the SSO Debug Flow component. Manipulating this component can lead to missing authentication, and the attack can be executed remotely. The exploit has been publicly disclosed and may be used, and the vendor was contacted early about the disclosure.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

CVSS score: 7.3(medium)
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L
Attack vector: network
Attack complexity: low
Privileges required: none
User interaction: none
EPSS: 0.8%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on October 10, 2026.
Disclosure date: June 21, 2026

Weaknesses and techniques

Weakness (CWE): CWE-287
Attack pattern (CAPEC): CAPEC-114

Affected

Packages: litellm@<= 1.82.2
Vendors: LangChain

Classification

Attack type: Other
Attack sophistication: Trivial
AI component targeted: API
Impact: Confidentiality,
Integrity
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative.

Original source: https://github.com/advisories/GHSA-j37q-q7p9-vpwm

First tracked: October 10, 2026 at 12:52 AM ET, from GitHub Advisory Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 90% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

litellmPyPILLM dependency since 2023-07-27 · 54 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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