Vulnerability · Published Nov 21, 2025 · Assigned by GitHub_M
CVE-2025-62164VLLM deserialization vulnerability leading to DoS and potential RCE
High 8.8
In plain English
A high-severity improper input validation in vllm from vllm-project. There are no confirmed reports of exploitation so far. The chance it is exploited in the next 30 days is estimated at 1.0% (higher than 62% of all known vulnerabilities). Fixed versions are available - upgrade the affected package.
Official 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.
Affected packages & fixed versions
| Ecosystem | Package | Fixed in | Advisory |
|---|---|---|---|
| PyPI | vllm | 0.11.1 | GHSA-mrw7-hf4f-83pf |
| PyPI | vllm | 0.11.1 | PYSEC-2026-2018 |
Affected products (as reported)
| Vendor | Product | Versions |
|---|---|---|
| vllm-project | vllm | >= 0.10.2, < 0.11.1 |
Weakness: CWE-20, CWE-123, CWE-502, CWE-787 · Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
References
- https://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pf x_refsource_CONFIRM
- https://github.com/vllm-project/vllm/pull/27204 x_refsource_MISC
- https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b x_refsource_MISC