5.9

CVE-2026-34760

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.
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Herstellervllm-project
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Produkt vllm
Version >= 0.5.5, < 0.18.0
Status affected
Zu dieser CVE wurde keine CISA KEV oder CERT.AT-Warnung gefunden.
EPSS Metriken
Typ Quelle Score Percentile
EPSS FIRST.org 0.06% 0.2
CVSS Metriken
Quelle Base Score Exploit Score Impact Score Vector String
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.