CVE-2025-62164vLLM is an inference and serving engine for large language models (LLMs).
Updated under an hour ago ()
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No exploitation reported and no EPSS estimate yet.
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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.
- CVSS
- 8.8 HIGH (v3.1, NVD)CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
- Weakness (CWE)
- CWE-20, CWE-123, CWE-502, CWE-787
- Known exploited
- Not in CISA's KEV catalog
- EPSS (exploitation in the next 30 days)
- Not loaded for this CVE
- Assigned by
- Not given
- NVD analysis
- Analyzed
- Record last updated
- Jun 17, 2026
- First seen by InfoSec Signals
- Oct 9, 2026
Affected products
vllm vllm vllm:vllm | from 0.10.2 before 0.11.1; 0.11.1; 0.11.1 | NVD |
References
- https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84bpatch
- https://github.com/vllm-project/vllm/pull/27204issue-tracking, patch, vendor-advisory
- https://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pfissue-tracking, vendor-advisory
The CVE record on cve.org NVD's entry
The CVE record is authoritative. Products marked “from the name” were identified from the CNA's free-text names, not a CPE, and are matched less certainly. This product uses the NVD API but is not endorsed or certified by the NVD.