vLLM up to 0.17.x numpy.mean input validation

Summaryinfo

A vulnerability classified as problematic has been found in vLLM up to 0.17.x. This affects the function numpy.mean. Performing a manipulation results in input validation. This vulnerability is identified as CVE-2026-34760. The attack can be initiated remotely. There is not any exploit available. It is recommended to upgrade the affected component.

Detailsinfo

A vulnerability was found in vLLM up to 0.17.x. It has been classified as problematic. This affects the function numpy.mean. The manipulation with an unknown input leads to a input validation vulnerability. CWE is classifying the issue as CWE-20. The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly. This is going to have an impact on integrity, and availability. The summary by CVE is:

vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing (to_mono), while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results in inconsistency between audio heard by humans (e.g., through headphones/regular speakers) and audio processed by AI models (Which infra via Librosa, such as vllm, transformer). This issue has been patched in version 0.18.0.

It is possible to read the advisory at github.com. This vulnerability is uniquely identified as CVE-2026-34760 since 03/30/2026. The exploitability is told to be difficult. It is possible to initiate the attack remotely. Technical details of the vulnerability are known, but there is no available exploit.

Upgrading to version 0.18.0 eliminates this vulnerability. The upgrade is hosted for download at github.com. Applying the patch c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4 is able to eliminate this problem. The bugfix is ready for download at github.com. The best possible mitigation is suggested to be upgrading to the latest version.

Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

Name

• vLLM

Version

• 0.1
• 0.2
• 0.3
• 0.4
• 0.5
• 0.6
• 0.7
• 0.8
• 0.9
• 0.10
• 0.11
• 0.12
• 0.13
• 0.14
• 0.15
• 0.16
• 0.17

License

• open-source

CPE 2.3info

• 🔒
• 🔒
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CPE 2.2info

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• 🔒
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