Skip to content
Fencer Intel
high 7.1

CVE-2025-66448 json

vLLM vulnerable to remote code execution via transformers_utils/get_config

published 11mo ago modified 22d ago also EUVD-2025-200115 , GHSA-8fr4-5q9j-m8gm , PYSEC-2026-2015

cvss
7.1
version 3.1, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.7%
EPSS 0.7% chance of exploitation in 30 days, percentile 53%
public exploits
0
none known

Summary

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.11.1, vllm has a critical remote code execution vector in a config class named Nemotron_Nano_VL_Config. When vllm loads a model config that contains an auto_map entry, the config class resolves that mapping with get_class_from_dynamic_module(...) and immediately instantiates the returned class. This fetches and executes Python from the remote repository referenced in the auto_map string. Crucially, this happens even when the caller explicitly sets trust_remote_code=False in vllm.transformers_utils.config.get_config. In practice, an attacker can publish a benign-looking frontend repo whose config.json points via auto_map to a separate malicious backend repo; loading the frontend will silently run the backend’s code on the victim host. This vulnerability is fixed in 0.11.1.

Patch / fix

  • pypi vllm 0.11.1
  • vllm-project vllm 0.11.1
  • vllm vllm 0.11.1

Is CVE-2025-66448 in your dependencies? Fencer shows which of your repositories are affected as soon as an advisory lands, and opens the fix as a pull request.

Check with Fencer

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-2025-66448 updated cvss 7.5

vllm: vLLM: Remote Code Execution via malicious model configuration

  • Red Hat Enterprise Linux AI (RHEL AI) rhelai1/bootc-amd-rhel9, rhelai1/bootc-intel-rhel9, rhelai1/bootc-nvidia-rhel9, rhelai1/bootc-azure-amd-rhel9, rhelai1/instructlab-amd-rhel9, rhelai1/bootc-aws-nvidia-rhel9, +4 more Red Hat Enterprise Linux AI (RHEL AI) 3 rhelai3/bootc-cuda-rhel9, rhelai3/bootc-aws-cuda-rhel9, rhelai3/bootc-gcp-cuda-rhel9, rhelai3/bootc-azure-cuda-rhel9
    affected: workaround, no fix planned “Red Hat has investigated whether a possible mitigation exists for this issue, and has not been able to identify a practical example. Please update the affected package as soon as possible.”
  • Red Hat OpenShift AI (RHOAI) rhoai/odh-vllm-cuda-rhel9, rhoai/odh-vllm-rocm-rhel9
    affected: workaround, no fix available “Red Hat has investigated whether a possible mitigation exists for this issue, and has not been able to identify a practical example. Please update the affected package as soon as possible.”
  • Red Hat AI Inference Server 3.2 rhaiis 1772160593, rhaiis 1772160625, rhaiis 3.2.2-1764871796, rhaiis 3.2.2-1765379049, rhaiis 3.2.2-1765379088, rhaiis 3.2.5-1765361180, +3 more
    fixed: vendor fix “Red Hat has investigated whether a possible mitigation exists for this issue, and has not been able to identify a practical example. Please update the affected package as soon as possible.” RHSA-2025:23080
  • Red Hat AI Inference Server 3.3 rhaiis/vllm-spyre-rhel9 1782352919
    fixed: vendor fix “Red Hat has investigated whether a possible mitigation exists for this issue, and has not been able to identify a practical example. Please update the affected package as soon as possible.” RHSA-2026:30087
  • Red Hat OpenShift AI 2.25 rhoai 1772093436
    fixed: vendor fix “Red Hat has investigated whether a possible mitigation exists for this issue, and has not been able to identify a practical example. Please update the affected package as soon as possible.” RHSA-2026:3782
  • Red Hat OpenShift AI 3.3 rhoai 1770754585, rhoai 1770754605, rhoai 1770754646, rhoai 1770956034, rhoai 1771840993, rhoai 1778264363
    fixed: vendor fix “Red Hat has investigated whether a possible mitigation exists for this issue, and has not been able to identify a practical example. Please update the affected package as soon as possible.” RHSA-2026:3713
  • Red Hat OpenShift AI 2.25 rhoai 1772093237, rhoai 1772093250, rhoai 1772093252, rhoai 1772093260, rhoai 1772093266, rhoai 1772093272, +63 more Red Hat OpenShift AI 3.3 rhoai 1769162595, rhoai 1769436305, rhoai 1769519975, rhoai 1770277795, rhoai 1770735854, rhoai 1770788873, +148 more
    not 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
yes
Technical impact
total
Assessed
11mo ago

Exploit prediction

0.71% chance of exploitation in the next 30 days, higher than 53% of all scored CVEs (FIRST EPSS, 2026-11-09).

CVSS scores

assigned by version score vector
CNA 3.1 7.1 CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H
GitHub 3.1 7.1 CVSS:3.1/AV:N/AC:H/PR:L/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 7.1 CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H

Affected packages

ecosystem package affected fixed in
pypi vllm
<0.11.1
0.11.1

Affected products

  • vllm-project vllm

Classification

Weakness
  • CWE-94 Improper Control of Generation of Code

References

Sources

Fencer Intel 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.