{"dataType":"CVE_RECORD","dataVersion":"5.2","cveMetadata":{"cveId":"CVE-2026-115913","assignerOrgId":"a0819718-46f1-4df5-94e2-005712e83aaa","state":"PUBLISHED","assignerShortName":"GitHub_M","dateReserved":"2026-10-23T20:21:45.107Z","datePublished":"2026-10-26T16:56:59.817Z","dateUpdated":"2026-10-26T16:56:59.817Z"},"containers":{"cna":{"title":"vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine","problemTypes":[{"descriptions":[{"cweId":"CWE-770","lang":"en","description":"CWE-770: Allocation of Resources Without Limits or Throttling","type":"CWE"}]}],"metrics":[{"cvssV3_1":{"attackComplexity":"LOW","attackVector":"NETWORK","availabilityImpact":"HIGH","baseScore":6.5,"baseSeverity":"MEDIUM","confidentialityImpact":"NONE","integrityImpact":"NONE","privilegesRequired":"LOW","scope":"UNCHANGED","userInteraction":"NONE","vectorString":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","version":"3.1"}}],"references":[{"name":"https://github.com/vllm-project/vllm/security/advisories/GHSA-8397-8qf6-f2gm","tags":["x_refsource_CONFIRM"],"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-8397-8qf6-f2gm"},{"name":"https://github.com/vllm-project/vllm/pull/60614","tags":["x_refsource_MISC"],"url":"https://github.com/vllm-project/vllm/pull/60614"},{"name":"https://github.com/vllm-project/vllm/commit/3d52578e13454de0bb8352cac988347cdcc89928","tags":["x_refsource_MISC"],"url":"https://github.com/vllm-project/vllm/commit/3d52578e13454de0bb8352cac988347cdcc89928"},{"name":"https://github.com/vllm-project/vllm/releases/tag/v0.32.0","tags":["x_refsource_MISC"],"url":"https://github.com/vllm-project/vllm/releases/tag/v0.32.0"}],"affected":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":"< 0.32.0","status":"affected"}]}],"providerMetadata":{"orgId":"a0819718-46f1-4df5-94e2-005712e83aaa","shortName":"GitHub_M","dateUpdated":"2026-10-26T16:56:59.817Z"},"descriptions":[{"lang":"en","value":"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."}],"source":{"advisory":"GHSA-8397-8qf6-f2gm","discovery":"UNKNOWN"}}}}