# CVE-2025-62164

## Summary

- **CVE ID:** CVE-2025-62164
- **Severity:** HIGH
- **CVSS Score:** 8.8 (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
- **CWE:** CWE-20, CWE-123, CWE-502, CWE-787
- **Published:** Nov 21, 2025
- **Last Modified:** Mar 12, 2026

## 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 Products

- vllm-project — vllm (>= 0.10.2, < 0.11.1)

## References

- [CNA](https://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pf)
- [CNA](https://github.com/vllm-project/vllm/pull/27204)
- [CNA](https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b)

## Exploitation Prediction (EPSS)

- **EPSS Score:** 0.89%
- **EPSS Percentile:** 57.3

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_Exported from OnDuty AI Vulnerability Intelligence on 2026-09-11._