CVE-2025-62164

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.

The following products are affected by CVE-2025-62164 vulnerability.

Here, you will find a curated list of external links that provide in-depth information to CVE-2025-62164.

URL	Resource
https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b
https://github.com/vllm-project/vllm/pull/27204
https://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pf
https://nvd.nist.gov/vuln/detail/CVE-2025-62164
https://www.cve.org/CVERecord?id=CVE-2025-62164