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.
Attack Parameters
Attack Vector
Network
Can be exploited remotely
Attack Complexity
High
Difficult to exploit
Privileges Required
Low
Basic privileges needed
User Interaction
None
No user interaction needed
Impact Assessment
Confidentiality
None
No data leak
Integrity
High
Complete data modification
Availability
Low
Partial disruption
CVSS Vector v3.1
Weakness Type (CWE)
References 4
https://github.com/vllm-project/vllm/commit/c7f98b4d0a63b32ed939e2b6dfaa8a626e9…
security-advisories@github.com
https://github.com/vllm-project/vllm/pull/37058
security-advisories@github.com
https://github.com/vllm-project/vllm/releases/tag/v0.18.0
security-advisories@github.com
https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8
security-advisories@github.com