AI Sec Watch: A Security Intelligence Platform for AI Systems

Luu, T.J.

HighVulnerability

GHSA-7ggm-4rjg-594w: litellm passes untrusted data to `eval` function without sanitization

Source: GitHub Advisory(opens in a new tab)
Identifiers: CVE-2024-4264GHSA-7ggm-4rjg-594w
Published: May 17, 2024
Record updated: October 10, 2026

View JSON

Affected: litellm <= 1.28.11
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: 0.9%

Summary

A remote code execution flaw in berriai/litellm stems from unsafe use of the `eval` function in the `litellm.get_secret()` method. When the server uses Google KMS, untrusted data reaches `eval` without sanitization. Attackers can inject malicious values into environment variables through the `/config/update` endpoint, which updates settings in `proxy_server_config.yaml`.

Mitigation

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

Details

Scores and exploitation

CVSS score: 7.2(high)
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H
Attack vector: network
Attack complexity: low
Privileges required: high
User interaction: none
EPSS: 0.9%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: May 17, 2024

Weaknesses and techniques

Weakness (CWE): CWE-94
Attack pattern (CAPEC): CAPEC-242

Affected

Packages: litellm@<= 1.28.11

Classification

Attack type: Other
Attack sophistication: Moderate
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-7ggm-4rjg-594w

First tracked: October 10, 2026 at 12:53 AM ET, from GitHub Advisory Database

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

Classifier confidence: 95% (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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