CVE-2026-34760
CVE-2026-34760 · vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models
Cette page présente des faits issus de sources publiques. Consultez l’avis original avant toute mise à jour.
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.
CVSS 5.9 · Moyenne · EPSS 0,3% · Correctif disponible
- Publication
- Mise à jour
Pile logicielle: python
Produits et versions concernés
vllm
- Versions du logiciel
- >= 0.5.5, < 0.18.0: Concernée
Aucun correctif publié
vllm PyPI
- Versions du logiciel
- 0.10.0
- 0.10.1
- 0.10.1.1
- 0.10.2
- 0.11.0
- 0.11.1
- 0.11.2
- 0.12.0
- 0.13.0
- 0.14.0
- 0.14.1
- 0.15.0
- 0.15.1
- 0.16.0
- 0.17.0
- 0.17.1
- 0.5.5
- 0.6.0
- 0.6.1
- 0.6.1.post1
- 0.6.1.post2
- 0.6.2
- 0.6.3
- 0.6.3.post1
- 0.6.4
- 0.6.4.post1
- 0.6.5
- 0.6.6
- 0.6.6.post1
- 0.7.0
- 0.7.1
- 0.7.2
- 0.7.3
- 0.8.0
- 0.8.1
- 0.8.2
- 0.8.3
- 0.8.4
- 0.8.5
- 0.8.5.post1
- 0.9.0
- 0.9.0.1
- 0.9.1
- 0.9.2
- Plages de versions concernées
- ECOSYSTEM: ≥ 0.5.5, < 0.18.0
Mettez à jour vers 0.18.0
Sources originales
- https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8
- https://github.com/vllm-project/vllm/pull/37058
- https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9b46c4
- https://github.com/vllm-project/vllm/releases/tag/v0.18.0
- https://nvd.nist.gov/vuln/detail/CVE-2026-34760