Blind SQL Injection in berriai/litellm
Description
A blind SQL injection vulnerability exists in the berriai/litellm application, specifically within the '/team/update' process. The vulnerability arises due to the improper handling of the 'user_id' parameter in the raw SQL query used for deleting users. An attacker can exploit this vulnerability by injecting malicious SQL commands through the 'user_id' parameter, leading to potential unauthorized access to sensitive information such as API keys, user information, and tokens stored in the database. The affected version is 1.27.14.
AI Insight
LLM-synthesized narrative grounded in this CVE's description and references.
A blind SQL injection in LiteLLM's /team/update endpoint allows an unauthenticated attacker to extract sensitive data via the user_id parameter.
A blind SQL injection vulnerability exists in the berriai/litellm application, specifically within the '/team/update' process. The vulnerability arises due to the improper handling of the 'user_id' parameter in the raw SQL query used for deleting users. An attacker can exploit this vulnerability by injecting malicious SQL commands through the 'user_id' parameter, leading to potential unauthorized access to sensitive information such as API keys, user information, and tokens stored in the database. The affected version is 1.27.14 [1][3].
Root
Cause
The issue is that the user_id parameter is concatenated directly into a raw SQL query without parameterization. This allows an attacker to inject arbitrary SQL commands. The vulnerability is classified as a blind SQL injection, meaning the attacker does not receive direct database output but can infer information based on the application's response behavior [1][3].
Exploitation and
Impact
An attacker can exploit this vulnerability by crafting a malicious user_id value in the request to the /team/update endpoint. No authentication is required for this particular operation, making it accessible to anyone who can reach the API. Successful exploitation allows the attacker to extract sensitive data from the database, including API keys, user information, and tokens. This could lead to a full compromise of the LiteLLM proxy server and any connected LLM provider credentials [1][3][4].
Mitigation
The maintainers have addressed this issue in pull request #2954 by switching from raw SQL to parameterized queries, which prevents SQL injection [2]. Users should upgrade to a patched version immediately. There is no known workaround; the only effective mitigation is to apply the fix from the pull request or update to a version that includes it [2][4].
- GitHub - BerriAI/litellm: Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]
- [security fix - Low severity] - team update use parameterized vals by ishaan-jaff · Pull Request #2954 · BerriAI/litellm
- NVD - CVE-2024-4890
- The world’s first bug bounty platform for AI/ML
AI Insight generated on May 20, 2026. Synthesized from this CVE's description and the cited reference URLs; citations are validated against the source bundle.
Affected packages
Versions sourced from the GitHub Security Advisory.
| Package | Affected versions | Patched versions |
|---|---|---|
litellmPyPI | <= 1.27.14 | — |
Affected products
2- berriai/berriai/litellmv5Range: unspecified
Patches
0No patches discovered yet.
Vulnerability mechanics
AI mechanics synthesis has not run for this CVE yet.
References
4News mentions
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