CVE-2026-22807 (GCVE-0-2026-22807)

Vulnerability from cvelistv5 – Published: 2026-01-21 21:13 – Updated: 2026-07-21 12:05
VLAI
Title
vLLM affected by RCE via auto_map dynamic module loading during model initialization
Summary
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.
Severity
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
  • CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
Impacted products
Vendor Product Version
vllm-project vllm Affected: >= 0.10.1, < 0.14.0
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Red Hat Red Hat AI Inference Server 3.2 Unaffected: 1772160593 , < * (rpm)
    cpe:/a:redhat:ai_inference_server:3.2::el9
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Red Hat Red Hat AI Inference Server 3.2 Unaffected: 1772160625 , < * (rpm)
    cpe:/a:redhat:ai_inference_server:3.2::el9
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Red Hat Red Hat AI Inference Server 3.3 Unaffected: 1782352919 , < * (rpm)
    cpe:/a:redhat:ai_inference_server:3.3::el9
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Red Hat Red Hat AI Inference Server 3.3 Unaffected: 1782353093 , < * (rpm)
    cpe:/a:redhat:ai_inference_server:3.3::el9
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Red Hat Red Hat AI Inference Server 3.3 Unaffected: 1782352847 , < * (rpm)
    cpe:/a:redhat:ai_inference_server:3.3::el9
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Red Hat Red Hat OpenShift AI 2.25 Unaffected: 1776259063 , < * (rpm)
    cpe:/a:redhat:openshift_ai:2.25::el9
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Red Hat Red Hat OpenShift AI 2.25 Unaffected: 1772093278 , < * (rpm)
    cpe:/a:redhat:openshift_ai:2.25::el9
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Red Hat Red Hat OpenShift AI 2.25 Unaffected: 1783998774 , < * (rpm)
    cpe:/a:redhat:openshift_ai:2.25::el9
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Red Hat Red Hat OpenShift AI 2.25 Unaffected: 1783998857 , < * (rpm)
    cpe:/a:redhat:openshift_ai:2.25::el9
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Red Hat Red Hat OpenShift AI 3.3 Unaffected: 1770956034 , < * (rpm)
    cpe:/a:redhat:openshift_ai:3.3::el9
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Red Hat Red Hat OpenShift AI 3.4 Unaffected: 1772204861 , < * (rpm)
    cpe:/a:redhat:openshift_ai:3.4::el9
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Red Hat Red Hat AI Inference Server     cpe:/a:redhat:ai_inference_server:3
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Red Hat Red Hat Enterprise Linux AI (RHEL AI) 3     cpe:/a:redhat:enterprise_linux_ai:3
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Red Hat Red Hat OpenShift AI (RHOAI)     cpe:/a:redhat:openshift_ai
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        "advisory": "GHSA-2pc9-4j83-qjmr",
        "discovery": "UNKNOWN"
      },
      "title": "vLLM affected by RCE via auto_map dynamic module loading during model initialization"
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    "cveId": "CVE-2026-22807",
    "datePublished": "2026-01-21T21:13:11.894Z",
    "dateReserved": "2026-01-09T22:50:10.288Z",
    "dateUpdated": "2026-07-21T12:05:15.590Z",
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}



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Taxonomy of the tags.





Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.


Detection rules are retrieved from Rulezet.