Memory Corruption Vulnerability in vLLM Inference Engine from Vendor vLLM
CVE-2025-62164

8.8HIGH

Key Information:

Vendor

Vllm-project

Status
Vllm
Vendor
CVE Published:
21 November 2025

What is CVE-2025-62164?

The vLLM inference and serving engine for large language models contains a memory corruption vulnerability in its Completions API endpoint. The flaw arises from insufficient validation when loading user-supplied prompt embeddings with torch.load(). A security change in PyTorch 2.8.0 disables certain tensor integrity checks by default, enabling potentially malicious tensors to bypass internal bounds checks. This can lead to an out-of-bounds memory write, resulting in a denial-of-service situation or, in some cases, remote code execution on the server hosting the vLLM engine. Users of vLLM versions 0.10.2 to before 0.11.1 should upgrade to version 0.11.1 to mitigate this risk.

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Affected Version(s)

vllm >= 0.10.2, < 0.11.1

References

https://github.com/vllm-project/vllm/security/advisories/...
x_refsource_CONFIRM
https://github.com/vllm-project/vllm/pull/27204
x_refsource_MISC
https://github.com/vllm-project/vllm/commit/58fab50d82838...
x_refsource_MISC

CVSS V3.1

Score:
8.8
Severity:
HIGH
Confidentiality:
High
Integrity:
High
Availability:
High
Attack Vector:
Network
Attack Complexity:
Low
Privileges Required:
Low
User Interaction:
None
Scope:
Unchanged

Timeline

  • Vulnerability published

  • Vulnerability Reserved

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