Medium severity · published Oct 26, 2026
CVE-2026-115913
vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine
New
6.5
CVSS
0.3%
EPSS
No
On KEV
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.
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Attack vector
Network
Complexity
Low
Privileges
Low
User interaction
None
Confidentiality
None
Integrity
None
Availability
High
Scope
Unchanged
Exploitation
Is it being used?
Exploit prediction (EPSS)0.3% · higher than 23% of CVEs
Exploited in the wildNo evidence yet
CISA KEVNot listed
Ransomware campaign useNot known
Affected products
Which versions
VL
vllm · vllm-project
< 0.32.0 · source: vendor
References
NVD
https://nvd.nist.gov/vuln/detail/CVE-2026-115913
CVE.org
https://www.cve.org/CVERecord?id=CVE-2026-115913
github.com
https://github.com/vllm-project/vllm/commit/3d52578e13454de0bb8352cac988347cdcc89928
github.com
https://github.com/vllm-project/vllm/pull/60614
github.com
https://github.com/vllm-project/vllm/releases/tag/v0.32.0
github.com
https://github.com/vllm-project/vllm/security/advisories/GHSA-8397-8qf6-f2gm
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