CVE-2025-62164 (GCVE-0-2025-62164)

Vulnerability from cvelistv5 – Published: 2025-11-21 01:18 – Updated: 2025-11-24 18:12
VLAI?
Title
VLLM deserialization vulnerability leading to DoS and potential RCE
Summary
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.
Severity ?
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
CWE
  • CWE-20 - Improper Input Validation
  • CWE-123 - Write-what-where Condition
  • CWE-502 - Deserialization of Untrusted Data
  • CWE-787 - Out-of-bounds Write
Assigner
References
Impacted products
Vendor Product Version
vllm-project vllm Affected: >= 0.10.2, < 0.11.1
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  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
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  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.



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