GitHub Advisoryadvisory · PyPI
GHSA-7972-pg2x-xr59 vLLM has Hardcoded Trust Override in Model Files Enables RCE Despite Explicit User Opt-Out
HIGH
Official page ↗
- Aliases
- CVE-2026-27893
- Ecosystem
- PyPI
- Packages
- vllm
- Severity
- High
- CVSS vector
- CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
- Weaknesses
- CWE-693
- Reviewed
- 2026-03-27
Affected packages
• PyPI vllm: >= 0.10.1, < 0.18.0 → fixed in 0.18.0
Description
Summary
Two model implementation files hardcode `trust_remote_code=True` when loading sub-components, bypassing the user's explicit `--trust-remote-code=False` security opt-out. This enables remote code execution via malicious model
repositories even when the user has explicitly disabled remote code trust.
### Details
Affected files (latest main branch):
1. `vllm/model_executor/models/nemotron_vl.py:430`
```python
vision_model = AutoModel.from_config(config.vision_config, trust_remote_code=True)
```
2. vllm/model_executor/models/kimi_k25.py:177
```python
cached_get_image_processor(self.ctx.model_config.model, trust_remote_code=True)
```
Both pass a hardcoded trust_remote_code=True to HuggingFace API calls, overriding the user's global --trust-remote-code=False setting.
Relation to prior CVEs:
• CVE-2025-66448 fixed auto_map resolution in vllm/transformers_utils/config.py (config loading path)
• CVE-2026-22807 fixed broader auto_map at startup
• Both fixes are present in the current code. These hardcoded instances in model files survived both patches — different code paths.
Impact
Remote code execution. An attacker can craft a malicious model repository that executes arbitrary Python code when loaded by vLLM, even when the user has explicitly set --trust-remote-code=False. This undermines the security guarantee
that trust_remote_code=False is intended to provide.
Remediation: Replace hardcoded trust_remote_code=True with self.config.model_config.trust_remote_code in both files. Raise a clear error if the model component requires remote code but the user hasn't opted in.
Related
Alias · CVE (1)
- CVE-2026-27893 vLLM is an inference and serving engine for large language models (LLMs). vulnerability
Weakness · CWE (1)
- CWE-693 Protection Mechanism Failure weakness
References
- https://github.com/vllm-project/vllm/security/advisories/GHSA-7972-pg2x-xr59 web
- https://nvd.nist.gov/vuln/detail/CVE-2026-27893 advisory
- https://github.com/vllm-project/vllm/pull/36192 web
- https://github.com/vllm-project/vllm/commit/00bd08edeee5dd4d4c13277c0114a464011acf72 web
- https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-27893.json web
- https://github.com/vllm-project/vllm package
- https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-2297.yaml web
- https://bugzilla.redhat.com/show_bug.cgi?id=2452055 web
- https://access.redhat.com/security/cve/CVE-2026-27893 web
- https://access.redhat.com/errata/RHSA-2026:8748 web
- https://access.redhat.com/errata/RHSA-2026:8747 web
- https://access.redhat.com/errata/RHSA-2026:8746 web
- https://access.redhat.com/errata/RHSA-2026:37275 web
- https://access.redhat.com/errata/RHSA-2026:24977 web
- https://access.redhat.com/errata/RHSA-2026:19725 web
- https://access.redhat.com/errata/RHSA-2026:19724 web
- https://access.redhat.com/errata/RHSA-2026:19712 web
- https://access.redhat.com/errata/RHSA-2026:10141 web
- https://access.redhat.com/errata/RHSA-2026:10140 web