CVE-2025-62164
Last modified
CVE-2025-62164 is a high-severity vulnerability rated 8.8/10 on the CVSS scale. 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. EPSS estimates a 0.83% chance of exploitation in the next 30 days.
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.
Metrics
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Weakness Enumeration
Affected Software
| Vendor | Product | Versions | Update |
|---|---|---|---|
| Vllm | Vllm | >= 0.10.2, < 0.11.1 | — |
| Vllm | Vllm | 0.11.1 | Rc0 |
References
- 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
Timeline
- Published
- Last Modified
- Status
- Analyzed
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