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.
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Remediation
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References
History
Thu, 02 Apr 2026 20:30:00 +0000
| Type | Values Removed | Values Added |
|---|---|---|
| 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. | |
| Title | vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models | |
| Weaknesses | CWE-20 | |
| References |
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| Metrics |
cvssV3_1
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Subscriptions
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Status: PUBLISHED
Assigner: GitHub_M
Published:
Updated: 2026-04-02T18:59:49.638Z
Reserved: 2026-03-30T19:17:10.225Z
Link: CVE-2026-34760
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Status : Received
Published: 2026-04-02T20:16:25.437
Modified: 2026-04-02T20:16:25.437
Link: CVE-2026-34760
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OpenCVE Enrichment
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Weaknesses