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    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.

    Published:Oct 26, 2026
    Last Modified:Nov 2, 2026
    EPS:Oct 26, 2026
    EPSS Score:0.00329
    CVSS Score:6.5

    Affected Products

    Vendor
    Vllm
    Product
    Vllm
    Vendor
    Vllm-project
    Product
    Vllm

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    Attack Vector
    Network
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    Physical
    Privileges Required
    None
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    High
    User Interaction
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    Scope
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    Changed
    Confidentiality
    None
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    Integrity
    None
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    Availability
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    High