CVE-2026-115913

Published: Ott 26, 2026 Last Modified: Ott 26, 2026
ExploitDB:
Other exploit source:
Google Dorks:
MEDIUM 6,5
Attack Vector: network
Attack Complexity: low
Privileges Required: low
User Interaction: none
Scope: unchanged
Confidentiality: none
Integrity: none
Availability: high

Description

vLLM is an inference and serving engine for large language models. Prior to 0.32.0, the Phi-4 multimodal audio encoder in phi4mm_audio.py and phi4mm_utils.py uses eager attention by default, allowing a remote client to submit one legal long audio clip that causes MultiHeadedAttention.forward_attention to materialize and retain full attention matrices in self.attn. The scaled dot-product attention branch does not materialize these matrices. The resulting quadratic GPU-memory use can cause an out-of-memory condition and terminate EngineCore for all users. This issue is fixed in version 0.32.0.

770

Allocation of Resources Without Limits or Throttling

Incomplete
Common Consequences
Security Scopes Affected:
Availability
Potential Impacts:
Dos: Resource Consumption (Cpu) Dos: Resource Consumption (Memory) Dos: Resource Consumption (Other)
Applicable Platforms
All platforms may be affected
View CWE Details
https://github.com/vllm-project/vllm/commit/3d52578e13454de0bb8352cac988347cdcc…
https://github.com/vllm-project/vllm/pull/60614
https://github.com/vllm-project/vllm/releases/tag/v0.32.0
https://github.com/vllm-project/vllm/security/advisories/GHSA-8397-8qf6-f2gm