Evidence used
- No CISA KEV confirmation is currently recorded.
- EPSS is 0.70% for the current model date.
BlackTreeCVE Intelligencevllm-project · vllm
Official source article: GitHub GHSA-RH4J-5RHW-HR54 ↗. Check the applicable product and release in the original source.
High technical severity; prioritise exposed affected systems while verifying vendor guidance.
These OSV and GitHub advisory ranges apply only to the named package and ecosystem. A listed fixed version is not a universal product patch or proof that an update is installed.
| Ecosystem and package | Affected range | First fixed version | Evidence |
|---|---|---|---|
| pipvllm | < 0.7.0 | 0.7.0 | GitHub advisory ↗upstream repository advisory · 30 Jun 2025 |
High technical severity; prioritise exposed affected systems while verifying vendor guidance.
Patch availablevLLM 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.
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.
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may execute code or commands in the affected security context.
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.
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may execute code or commands in the affected security context.
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.
CWE-502: Deserialization of Untrusted Data. The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:HCommon Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.
Operational remediation based on structured source evidence.
Published 27 Jan 2025 · Last source change 12 Feb 2025, 20:41 UTC · CWE-502 · Deserialization of Untrusted Data
Core structured fields are present and their contributing authorities are shown above.
No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.