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
Configurations
No configuration.
History
21 Nov 2025, 02:15
| Type | Values Removed | Values Added |
|---|---|---|
| New CVE |
Information
Published : 2025-11-21 02:15
Updated : 2025-11-21 15:13
NVD link : CVE-2025-62164
Mitre link : CVE-2025-62164
CVE.ORG link : CVE-2025-62164
JSON object : View
Products Affected
No product.
