vLLM vulnerable to remote code execution via transformers_utils/get_configGHSA-8fr4-5q9j-m8gm
HIGHFix: vllm-project/vllm#28126GHSA-8fr4-5q9j-m8gm is a high-severity (CVSS 7.1) Code Injection vulnerability in vllm. A fix is available for vllm — see the affected versions and patch details below.
Exploitation Status
No confirmed exploitation observed yet
- CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
- A successful exploit gives an attacker total control of the affected component, not partial access.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for GHSA-8fr4-5q9j-m8gm.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
Where this sits among everything scored
Real-World Exposure
vllmReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Summary
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.
Details
The vulnerable code resolves and instantiates classes from auto_map entries without checking whether those entries point to a different repo or whether remote code execution is allowed.
class Nemotron_Nano_VL_Config(PretrainedConfig):
model_type = 'Llama_Nemotron_Nano_VL'
def __init__(self, **kwargs):
super().__init__(**kwargs)
if vision_config is not None:
assert "auto_map" in vision_config and "AutoConfig" in vision_config["auto_map"]
# <-- vulnerable dynamic resolution + instantiation happens here
vision_auto_config = get_class_from_dynamic_module(*vision_config["auto_map"]["AutoConfig"].split("--")[::-1])
self.vision_config = vision_auto_config(**vision_config)
else:
self.vision_config = PretrainedConfig()
get_class_from_dynamic_module(...) is capable of fetching and importing code from the Hugging Face repo specified in the mapping. trust_remote_code is not enforced for this code path. As a result, a frontend repo can redirect the loader to any backend repo and cause code execution, bypassing the trust_remote_code guard.
Impact
This is a critical vulnerability because it breaks the documented trust_remote_code safety boundary in a core model-loading utility. The vulnerable code lives in a common loading path, so any application, service, CI job, or developer machine that uses vllm’s transformer utilities to load configs can be affected. The attack requires only two repos and no user interaction beyond loading the frontend model. A successful exploit can execute arbitrary commands on the host.
Fixes
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | vllm | all versions | 0.11.1pip install --upgrade 'vllm==0.11.1' |
Affected Products
vllmvllmDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update vllm to 0.11.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-8fr4-5q9j-m8gm is resolved across your whole dependency graph.
Workarounds
Stop passing untrusted input into the interpreter or shell: call the affected binary with an argument array rather than a composed command string, reject anything outside a strict allowlist of expected values, and run the component under an account that cannot reach beyond the work it legitimately does.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
This vulnerability is rated Important for Red Hat because vLLM, when deployed in a Red Hat environment, is susceptible to remote code execution. An attacker can craft a malicious model configuration that, when loaded, fetches and executes arbitrary Python code from a remote repository, even if `trust_remote_code` is…
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.Source: Red Hat security advisory for GHSA-8fr4-5q9j-m8gm (CC BY 4.0)
| Product | Fixed in | Advisory |
|---|---|---|
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-cuda-rhel9:3.2.2-1765379088 | RHSA-2025:23078 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-rocm-rhel9:3.2.2-1765379049 | RHSA-2025:23079 |
| Red Hat AI Inference Server 3.2 | rhaiis/model-opt-cuda-rhel9:3.2.2-1764871796 | RHSA-2025:23080 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-cuda-rhel9:3.2.5-1765552580 | RHSA-2025:23204 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-rocm-rhel9:3.2.5-1765361180 | RHSA-2025:23205 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-tpu-rhel9:3.2.5-1765552619 | RHSA-2025:23209 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-rocm-rhel9:3.2.5-1765552603 | RHSA-2025:23449 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-cuda-rhel9:1772160593 | RHSA-2026:3461 |
Frequently Asked Questions
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