FKIE_CVE-2025-62164

Vulnerability from fkie_nvd - Published: 2025-11-21 02:15 - Updated: 2025-12-04 17:14
Severity ?
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Summary
vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation. Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1.
References
security-advisories@github.comhttps://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84bPatch
security-advisories@github.comhttps://github.com/vllm-project/vllm/pull/27204Issue Tracking, Patch, Vendor Advisory
security-advisories@github.comhttps://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pfVendor Advisory, Issue Tracking
Impacted products
Vendor Product Version
vllm vllm *
vllm vllm 0.11.1
vllm vllm 0.11.1
To clipboard 

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  "id": "CVE-2025-62164",
  "lastModified": "2025-12-04T17:14:20.630",
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