7.1

CVE-2026-34760

vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models

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.
Daten sind bereitgestellt durch National Vulnerability Database (NVD)
Vllm ≫ Vllm Version >= 0.5.5 < 0.18.0
VulnDex Vulnerability Enrichment
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Zu dieser CVE wurde keine Warnung gefunden.
EPSS Metriken
Typ Quelle Score Percentile
EPSS FIRST.org 0.27% 0.18
CVSS Metriken
Quelle Base Score Exploit Score Impact Score Vector String
nvd@nist.gov 7.1 2.8 4.2
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:L
security-advisories@github.com 5.9 1.6 4.2
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:L
CWE-20 Improper Input Validation

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.

https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8
Vendor Advisory
https://github.com/vllm-project/vllm/pull/37058
Issue Tracking
https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4
Patch
https://github.com/vllm-project/vllm/releases/tag/v0.18.0
Release Notes