LiteLLM

Malicious Code Injected into LiteLLM Open Source AI Package via Supply Chain Attack

The open-source generative AI tool LiteLLM had malicious code inserted into its official distribution packages, enabling credential theft and remote code execution. The compromise affected version 1.107.0 released on October 21, 2026, and was delivered through legitimate channels in a classic software supply chain attack. Seven malicious wheel files were uploaded to PyPI, while official container images on Docker Hub and GitHub Container Registry were also tainted. Security firm JFrog discovered that the malware steals a wide range of secrets including cloud credentials, SSH private keys, browser-stored passwords, development tokens, and AI service API keys from LiteLLM's database. The code also supports execution of externally fetched payloads and abuses Python's startup mechanisms to run even when the application itself is not loaded. This marks the second supply chain incident involving the project in 2026, following a separate event in March.

Check Point Discloses Two High-Impact Vulnerabilities in LiteLLM AI Gateway Allowing Low-Privilege Users to Steal API Keys

Check Point Research has disclosed two vulnerabilities, CVE-2026-35029 and CVE-2026-59823, in the LiteLLM AI gateway that affect all versions prior to 1.83.9. The flaws enable low-privilege authenticated users to modify gateway configurations, read server files, redirect traffic, and perform SSRF attacks to access internal resources. LiteLLM serves as a unified proxy for over 140 AI providers including OpenAI, Anthropic, and Azure OpenAI, managing API keys, routing, and usage monitoring for enterprises worldwide. The configuration update endpoint lacks role-based access controls, allowing any authenticated user to alter settings and potentially achieve remote code execution. Combined with the SSRF vulnerability, attackers can pivot from low-privilege accounts to compromise internal networks and exfiltrate stored credentials. The project has exceeded 61,000 GitHub stars and recorded over 90 million monthly downloads, making the issues particularly widespread due to its embedding in numerous internal AI platforms. Organizations are advised to upgrade immediately, audit access to management endpoints, rotate all API keys, and scan for hidden LiteLLM instances in requirements files and dependencies.

CISA Adds Seven Actively Exploited Vulnerabilities to KEV Catalog, Including SonicWall and LiteLLM Flaws Targeting AI Infrastructure

CISA has added seven vulnerabilities confirmed to be exploited in the wild to its Known Exploited Vulnerabilities catalog, with two carrying CVSS scores of 10.0. The list includes critical issues in SonicWall SMA 1000 devices, JFrog Artifactory, Kestra workflow engine, and LiteLLM AI gateway components. Attackers are now systematically targeting AI infrastructure to steal large language model API keys and configuration data from databases such as LiteLLM_ProxyModelTable. Qilin ransomware operators have already chained LiteLLM flaws for remote code execution and data theft. CISA has set a 48-hour remediation deadline for federal agencies, underscoring the urgency for all organizations running these components.

LiteLLM Supply Chain Poisoning Exposes 195TB of Credentials Across 2500 Organizations

A detailed forensic report from CloudSEK and Hudson Rock reveals that attackers compromised the LiteLLM CI/CD pipeline by poisoning the Trivy security scanner dependency. The malicious Trivy tag allowed theft of PyPI publishing tokens, leading to the upload of tainted LiteLLM versions 1.82.7 and 1.82.8. Within a 40-minute attack window these packages were downloaded over 119,000 times, exfiltrating 195TB of credentials including AWS, Azure, GCP keys, GitHub tokens, SSH keys, Kubernetes configs, and AI provider API keys. NVIDIA and multiple other major technology firms were confirmed among the victims. The incident highlights critical weaknesses in dependency pinning practices and the absence of automated detection for malicious package behavior on PyPI. Experts warn that AI infrastructure components are becoming high-value targets for future supply-chain campaigns.

How to Audit All Python Virtual Environments for Compromised Packages Without Executing Python

The article describes a practical workflow for discovering whether any Python virtual environments contain known malicious package versions. The author maintains a registry of all .venv directories across local disks and external volumes using find commands and shell hooks. A Bash script then iterates through the registry and runs uv pip freeze against each environment to list installed dependencies without invoking the Python interpreter. This approach avoids risks highlighted by recent supply-chain attacks on packages such as LiteLLM, where even python -V or pip freeze could trigger malicious .pth files. The method also supports locating outdated packages, identifying usage of deprecated libraries, and searching project code for specific functions. Configuration settings like PIP_REQUIRE_VIRTUALENV=true and the uv tool further prevent accidental global installations.

GitHub and PyPI Introduce Time-Based Defenses Against Supply Chain Attacks

GitHub and PyPI have activated new time-based barriers to slow down supply chain attacks. Dependabot now waits a default of 72 hours before proposing version updates, while PyPI rejects new files added to releases older than 14 days. The changes target non-security version updates and attempts to poison older stable releases. Security updates remain immediate, and the cooldown can be adjusted via dependabot.yml. The PyPI restriction, effective since July 8 2026, addresses risks from compromised tokens or CI/CD pipelines. Both platforms aim to give the community time to detect malicious packages before widespread adoption.