CVE-2026-115913

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.

• https://github.com/vllm-project/vllm/commit/3d52578e13454de0bb8352cac988347cdcc89928
• 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

• https://github.com/vllm-project/vllm/commit/3d52578e13454de0bb8352cac988347cdcc89928
• 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

• HIGHCVE-2023-54404PoC
• MEDIUMCVE-2025-12767
• MEDIUMCVE-2025-36061
• UNSCOREDCVE-2025-8352
• MEDIUMCVE-2026-100274
• MEDIUMCVE-2026-100350PoC
• MEDIUMCVE-2026-100508
• MEDIUMCVE-2026-100600PoC

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