Vllm
CVE-2025-24357
HIGH
Severity by source
AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H
Primary rating from GitHub Advisory.
CVSS VectorGitHub Advisory
Lifecycle Timeline
3DescriptionGitHub Advisory
vLLM is a library for LLM inference and serving. vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggingface. It uses the torch.load function and the weights_only parameter defaults to False. When torch.load loads malicious pickle data, it will execute arbitrary code during unpickling. This vulnerability is fixed in v0.7.0.
AnalysisAI
vLLM is a library for LLM inference and serving. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required. This Deserialization of Untrusted Data vulnerability could allow attackers to execute arbitrary code through malicious serialized objects.
Technical ContextAI
This vulnerability is classified as Deserialization of Untrusted Data (CWE-502), which allows attackers to execute arbitrary code through malicious serialized objects. vLLM is a library for LLM inference and serving. vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggingface. It uses the torch.load function and the weights_only parameter defaults to False. When torch.load loads malicious pickle data, it will execute arbitrary code during unpickling. This vulnerability is fixed in v0.7.0. Affected products include: Vllm.
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Avoid deserializing untrusted data. Use safe serialization formats (JSON). Implement integrity checks and type allowlists.
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Same weakness CWE-502 – Deserialization of Untrusted Data
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