CVE-2025-62164: vllm-project vllm Improper Input Validation

Vulnerability summary

CVE-2025-62164 is a high (CVSS 8.8) Improper Input Validation vulnerability in vllm-project vllm. EPSS estimates a 0.93% chance of exploitation in the next 30 days, higher than 59.9% of scored vulnerabilities.

Timeline

1. 21-11-2025
   PublishedVulnerability first disclosed.
2. 24-11-2025
   Last modifiedVulnerability information updated.

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.

• CWE-20Improper Input ValidationThe product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
• CWE-123Write-what-where ConditionAny condition where the attacker has the ability to write an arbitrary value to an arbitrary location, often as the result of a buffer overflow.
• CWE-502Deserialization of Untrusted DataThe product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
• CWE-787Out-of-bounds WriteThe product writes data past the end, or before the beginning, of the intended buffer.