What’s Vulnerable › Vulnerabilities

CVE-2026-34760: vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models

• Severity: high (CVSS 7.1 v3.1)
• Exploited in the wild: not listed in CISA KEV
• EPSS: 0.3% probability of exploitation in the next 30 days
• Public exploit or detection template: none known
• Published: 2026-04-02 · updated 2026-07-24
• Weakness: CWE-20

Description

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.

Affected products

• vllm-project vllm: from 0.5.5 before 0.18.0 — fixed in 0.18.0

How to fix

Upgrade to a fixed version: vllm-project vllm 0.18.0. Check the vendor advisory for mitigations.

References

• https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4
• https://github.com/vllm-project/vllm/pull/37058
• https://github.com/vllm-project/vllm/releases/tag/v0.18.0
• https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8

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