CVE-2025-24357 json
vLLM allows a malicious model RCE by torch.load in hf_model_weights_iterator
published 2025-01-27 21 mo ago modified 2026-08-07 3 mo ago also EUVD-2025-0192 , GHSA-rh4j-5rhw-hr54 , PYSEC-2025-58
- cvss
- 7.5
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.9%
- EPSS 0.9% chance of exploitation in 30 days, percentile 59%
- public exploits
- 0
- none known
Summary
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.
Patch / fix
- pypi vllm 0.7.0
- vllm-project vllm 0.7.0
- vllm vllm 0.7.0
- github.com/vllm-project/vllm@d3d6bb1 commit
- github.com/vllm-project/vllm#12366 pull request
- github.com/vllm-project/vllm/releases/tag/v0.7.0 release
Vendor advisories
What the vendors themselves say about their products: affected, fixed or not affected, and what to do.
red hat product security vex CVE-2025-24357 updated cvss 7.5
vllm: vLLM allows a malicious model RCE by torch.load in hf_model_weights_iterator
-
Red Hat Enterprise Linux AI (RHEL AI) bootc-intel-rhel9, bootc-nvidia-rhel9, bootc-aws-nvidia-rhel9, bootc-gcp-nvidia-rhel9, bootc-ibm-nvidia-rhel9, instructlab-intel-rhel9, +2 moreaffected: workaround, no fix available, no fix planned “Will not fix”
-
Red Hat Enterprise Linux AI (RHEL AI) bootc-azure-amd-rhel9, instructlab-amd-rhel9not affected: vulnerable code not present
Red Hat CSAF VEX © Red Hat, Inc., from https://security.access.redhat.com/data/csaf/v2/vex/; changed: reduced to product statuses and remediations. CC BY 4.0 · csaf
CISA SSVC
- Exploitation
- none
- Automatable
- no
- Technical impact
- total
- Assessed
- 21 mo ago
Exploit prediction
0.89% chance of exploitation in the next 30 days, higher than 59% of all scored CVEs (FIRST EPSS, 2026-11-08).
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 7.5 | CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H |
| GitHub | 3.1 | 7.5 | CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H |
| NVD | 3.1 | 8.8 | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H |
| OSV | 3.1 | 8.8 | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H |
Affected packages
| ecosystem | package | affected | fixed in |
|---|---|---|---|
| pypi | vllm | <0.7.0 |
0.7.0 |
Affected products
- vllm-project vllm
Classification
- Weakness
-
- CWE-502 Deserialization of Untrusted Data
References
-
github.com
/vllm-project/vllm/pull/12366
web, issue-tracking, patch, report
-
github.com
/vllm-project/vllm/commit/d3d6bb13fb62da3234addf6574922a4ec0513d04
web, patch
-
github.com
/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54
web, vendor-advisory, advisory
-
pytorch.org
/docs/stable/generated/torch.load.html
web, technical-description
-
nvd.nist.gov
/vuln/detail/CVE-2025-24357
advisory
-
github.com
/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-58.yaml
web
-
github.com
/vllm-project/vllm
package
-
github.com
/vllm-project/vllm/releases/tag/v0.7.0
web
Sources
- CVE List CVE-2025-24357
- GitHub Advisory GHSA-rh4j-5rhw-hr54
- NVD CVE-2025-24357
- OSV PYSEC-2025-58
Fencer Vulns keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.
CPEs and fixed versions use Anchore's correction of NVD's data: Added CPE configurations because not yet analyzed by NVD.