CVE-2026-34760

Published: Apr 02, 2026 Last Modified: Apr 02, 2026

ExploitDB:

Other exploit source:

Google Dorks:

MEDIUM 5,9

Source: [email protected]

Attack Vector: network

Attack Complexity: high

Privileges Required: low

User Interaction: none

Scope: unchanged

Confidentiality: none

Integrity: high

Availability: low

Description

AI Translation Available

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.

Improper Input Validation

Stable

Abstraction: Class Structure: Simple

Common Consequences

Security Scopes Affected:

Availability Confidentiality Integrity

Potential Impacts:

Dos: Crash, Exit, Or Restart Dos: Resource Consumption (Cpu) Dos: Resource Consumption (Memory) Read Memory Read Files Or Directories Modify Memory Execute Unauthorized Code Or Commands