CVE-2026-34760Improper Input Validation in Vllm

Severity
5.9MEDIUMNVD
EPSS
0.1%
top 80.13%
CISA KEV
Not in KEV
Exploit
No known exploits
Affected products
Timeline
PublishedApr 2

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

CVSS vector

CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:LExploitability: 1.6 | Impact: 4.2

Affected Packages1 packages

CVEListV5vllm-project/vllm>= 0.5.5, < 0.18.0

📋Vendor Advisories

1
Red Hat
vLLM: Librosa: numpy: Librosa: AI model data integrity impact due to audio processing discrepancy2026-04-02

💬Community

1
Bugzilla
CVE-2026-34760 vLLM: Librosa: numpy: Librosa: AI model data integrity impact due to audio processing discrepancy2026-04-02