vLLM affected by RCE via auto_map dynamic module loading during model initialization
Summary
| CVE | CVE-2026-22807 |
|---|---|
| State | PUBLISHED |
| Assigner | GitHub_M |
| Source Priority | CVE Program / NVD first with legacy fallback |
| Published | 2026-01-21 22:15:49 UTC |
| Updated | 2026-06-30 03:17:28 UTC |
| Description | vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.14.0, vLLM loads Hugging Face `auto_map` dynamic modules during model resolution without gating on `trust_remote_code`, allowing attacker-controlled Python code in a model repo/path to execute at server startup. An attacker who can influence the model repo/path (local directory or remote Hugging Face repo) can achieve arbitrary code execution on the vLLM host during model load. This happens before any request handling and does not require API access. Version 0.14.0 fixes the issue. |
Risk And Classification
Primary CVSS: v3.1 9.8 CRITICAL from [email protected]
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
EPSS: 0.007370000 probability, percentile 0.499530000 (date 2026-07-01)
Problem Types: CWE-94 | CWE-94 CWE-94: Improper Control of Generation of Code ('Code Injection') | CWE-94 Improper Control of Generation of Code ('Code Injection')
| Version | Source | Type | Score | Severity | Vector |
|---|---|---|---|---|---|
| 3.1 | [email protected] | Primary | 9.8 | CRITICAL | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H |
| 3.1 | ADP | CVSS | 8.8 | HIGH | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H |
| 3.1 | [email protected] | Secondary | 8.8 | HIGH | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H |
| 3.1 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | Secondary | 8.8 | HIGH | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H |
| 3.1 | CNA | DECLARED | 8.8 | HIGH | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H |
CVSS v3.1 Breakdown
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
NVD Known Affected Configurations (CPE 2.3)
Vendor Declared Affected Products
| Source | Vendor | Product | Version | Platforms |
|---|---|---|---|---|
| CNA | Vllm-project | Vllm | affected >= 0.10.1, < 0.14.0 | Not specified |
| ADP | Red Hat | Red Hat AI Inference Server 3.2 | Not specified | Not specified |
| ADP | Red Hat | Red Hat AI Inference Server 3.3 | Not specified | Not specified |
| ADP | Red Hat | Red Hat OpenShift AI 2.25 | Not specified | Not specified |
| ADP | Red Hat | Red Hat OpenShift AI 3.3 | Not specified | Not specified |
| ADP | Red Hat | Red Hat OpenShift AI 3.4 | Not specified | Not specified |
| ADP | Red Hat | Red Hat AI Inference Server | Not specified | Not specified |
| ADP | Red Hat | Red Hat Enterprise Linux AI RHEL AI 3 | Not specified | Not specified |
| ADP | Red Hat | Red Hat OpenShift AI RHOAI | Not specified | Not specified |
References
| Reference | Source | Link | Tags |
|---|---|---|---|
| access.redhat.com/security/cve/CVE-2026-22807 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| access.redhat.com/errata/RHSA-2026:3462 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| github.com/vllm-project/vllm/pull/32194 | [email protected] | github.com | Issue Tracking, Patch |
| access.redhat.com/errata/RHSA-2026:5119 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| access.redhat.com/errata/RHSA-2026:3782 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| access.redhat.com/errata/RHSA-2026:10184 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| github.com/vllm-project/vllm/commit/78d13ea9de4b1ce5e4d8a5af9738fea71fb0... | [email protected] | github.com | Patch |
| github.com/vllm-project/vllm/releases/tag/v0.14.0 | [email protected] | github.com | Product, Release Notes |
| access.redhat.com/errata/RHSA-2026:30089 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| access.redhat.com/errata/RHSA-2026:30087 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| access.redhat.com/errata/RHSA-2026:30088 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| access.redhat.com/errata/RHSA-2026:3713 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| github.com/vllm-project/vllm/security/advisories/GHSA-2pc9-4j83-qjmr | [email protected] | github.com | Patch, Vendor Advisory |
| security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-22807.json | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | security.access.redhat.com | |
| bugzilla.redhat.com/show_bug.cgi | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | bugzilla.redhat.com | |
| access.redhat.com/errata/RHSA-2026:3461 | 0b0ca135-0b70-47e7-9f44-1890c2a1c46c | access.redhat.com | |
| CVE Program record | CVE.ORG | www.cve.org | canonical |
| NVD vulnerability detail | NVD | nvd.nist.gov | canonical, analysis |
Additional Advisory Data
| Source | Time | Event |
|---|---|---|
| ADP | 2026-01-21T22:00:55.823Z | Reported to Red Hat. |
| ADP | 2026-01-21T21:13:11.894Z | Made public. |
Solutions
ADP: RHSA-2026:3461: Red Hat AI Inference Server 3.2
ADP: RHSA-2026:3462: Red Hat AI Inference Server 3.2
ADP: RHSA-2026:30089: Red Hat AI Inference Server 3.3
ADP: RHSA-2026:30088: Red Hat AI Inference Server 3.3
ADP: RHSA-2026:30087: Red Hat AI Inference Server 3.3
ADP: RHSA-2026:10184: Red Hat OpenShift AI 2.25
ADP: RHSA-2026:3782: Red Hat OpenShift AI 2.25
ADP: RHSA-2026:3713: Red Hat OpenShift AI 3.3
ADP: RHSA-2026:5119: Red Hat OpenShift AI 3.4
Workarounds
ADP: To mitigate this issue, ensure that vLLM instances are configured to load models only from trusted and verified repositories. Restrict access to the model repository path to prevent unauthorized modification or introduction of malicious code. Implement strict access controls and integrity checks for all model sources.