vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine
Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).
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Github GHSA |
GHSA-8397-8qf6-f2gm | vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine |
No CVSS v4.0
No CVSS v3.0
No CVSS v2
This CVE is not in the KEV list.
Exploit prediction score: < 1% (0.329% probability within 30 days)
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ssvc
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| CPEs | cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* |
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| First Time appeared |
Vllm
Vllm vllm Vllm-project Vllm-project vllm |
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Vllm
Vllm vllm Vllm-project Vllm-project vllm |
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| 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. | |
| Title | vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine | |
| Weaknesses | CWE-770 | |
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| Metrics |
cvssV3_1
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