| Component | vLLM |
| Severity | high (CVSS 8.8) |
| CVSS vector | CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H |
| Published (NVD) | 2025-11-21 |
| Last modified | 2026-06-17 |
| Affected versions | vllm >= 0.10.2, < 0.11.1 vllm = 0.11.1 vllm = 0.11.1 |
| Fixed in | 0.11.1 |
| Weakness | CWE-20CWE-123CWE-502CWE-787 |
| CISA KEV | not listed |
NVD description
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.
What to do
- Upgrade vLLM to
0.11.1or later. - Do not expose the component's API or UI to the internet without authentication in front of it.
Sources
- NVD: CVE-2025-62164
- https://github.com/vllm-project/vllm/pull/27204
- https://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pf
- https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b
Read more
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