AI Sec Watch: A Security Intelligence Platform for AI Systems

Luu, T.J.

MediumVulnerabilityLLM-specific

GHSA-8j42-pcfm-3467: SQL injection in litellm

Source: GitHub Advisory(opens in a new tab)
Identifiers: CVE-2024-4890GHSA-8j42-pcfm-3467
Published: June 6, 2024
Record updated: October 10, 2026

View JSON

Affected: litellm <= 1.27.14
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: 0.6%

Summary

A blind SQL injection vulnerability in the berriai/litellm application affects the '/team/update' process. The flaw stems from improper handling of the 'user_id' parameter in a raw SQL query used to delete users, letting an attacker inject SQL through that parameter. The affected version is 1.27.14.

Mitigation

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

Details

Scores and exploitation

CVSS score: 4.9(medium)
CVSS vector: CVSS:3.0/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:N/A:N
Attack vector: network
Attack complexity: low
Privileges required: high
User interaction: none
EPSS: 0.6%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 6, 2024

Weaknesses and techniques

Weakness (CWE): CWE-89
Attack pattern (CAPEC): CAPEC-66

Affected

Packages: litellm@<= 1.27.14

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: API
Impact: Confidentiality
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-8j42-pcfm-3467

First tracked: October 10, 2026 at 12:53 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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