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CVE-2025-62164

CVE-2025-62164 · VLLM deserialization vulnerability leading to DoS and potential RCE

This page contains facts from public vulnerability sources. Check the original advisory before updating.

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.

CVSS 8.8 · High · EPSS 1.0% · Fix available

Published
Updated

Stack: python

Affected products and versions

  • vllm

    Versions
    • >= 0.10.2, < 0.11.1: Affected

    No fix published

  • vllm PyPI

    Versions
    • 0.10.2
    • 0.11.0
    Affected ranges
    • ECOSYSTEM: ≥ 0.10.2, < 0.11.1

    Update to 0.11.1

Original sources

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