The Open Source Technology Improvement Fund is proud to share the results of our security audit of vLLM. vLLM is an open source Python library for Language Learning Machines (LLMs), running interference for large LLMs quickly and efficiently. vLLM is now one of the most actively used open source AI projects with thousands of contributors. Thanks to X41D-Sec and Alpha-Omega, this project underwent a source code audit to harden security and reinforce sustainability.
Audit Process:
In early 2026, X41-DSec security engineers executed a source code audit, working over several months on a white-box source code audit while also reviewing fuzz testing output to provide as much feedback and documentation as possible to the project. The primary question of the work was determining how attackers could compromise the project by access to either its underlying infrastructure or private data. The engagement was organized into sections, completed in the following order: Initial Design Workshop, Threat Modeling, Code Review, Documentation, GAP/Performance Analysis, and then Fixing and Mitigation.
Audit Results:
- 5 Findings with Security Impact
- 1 Critical
- 4 High
- 15 Findings without direct Security Impact
- Custom Security Documentation
- Future Security Development Recommendations
- Fuzz and Web API-Level Testing Review, Results, and Integration Recommendations
The vLLM team worked hard to address the issues raised by this report. Take advantage of the efforts performed by the X41 audit team and vLLM maintainers by updating to the most current release of vLLM. If you would like to learn more about the project or contribute, see their website: https://vllm.ai/
Thank you to the individuals and groups that made this engagement possible:
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vLLM maintainers and community, especially: Russell Bryant (Red Hat), Juan Perez de Algaba (Red Hat)
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X41D-Sec, especially: JJ, Markus Vervier, Niklas Abel, Yassine El Baaj, and Antonela Conti
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Alpha-Omega You can read the Audit Report HERE
You can read X41D-Sec’s Blog HERE
Everyone around the world depends on open source software. If you’re interested in supporting this critical work, reach out to us!