FKIE_CVE-2026-22807

Vulnerability from fkie_nvd - Published: 2026-01-21 22:15 - Updated: 2026-06-30 03:17
Severity
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
9.8 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
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.
References
security-advisories@github.comhttps://github.com/vllm-project/vllm/commit/78d13ea9de4b1ce5e4d8a5af9738fea71fb024e5Patch
security-advisories@github.comhttps://github.com/vllm-project/vllm/pull/32194Issue Tracking, Patch
security-advisories@github.comhttps://github.com/vllm-project/vllm/releases/tag/v0.14.0Product, Release Notes
security-advisories@github.comhttps://github.com/vllm-project/vllm/security/advisories/GHSA-2pc9-4j83-qjmrPatch, Vendor Advisory
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:10184
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:30087
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:30088
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:30089
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:3461
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:3462
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:3713
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:3782
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:5119
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/security/cve/CVE-2026-22807
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://bugzilla.redhat.com/show_bug.cgi?id=2431865
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-22807.json
Impacted products
Vendor Product Version
vllm vllm *
To clipboard 

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "vllm",
          "vendor": "vllm-project",
          "versions": [
            {
              "status": "affected",
              "version": "\u003e= 0.10.1, \u003c 0.14.0"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    },
    {
      "affectedData": [
        {
          "cpes": [
            "cpe:/a:redhat:ai_inference_server:3.2::el9"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat AI Inference Server 3.2",
          "vendor": "Red Hat"
        },
        {
          "cpes": [
            "cpe:/a:redhat:ai_inference_server:3.3::el9"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat AI Inference Server 3.3",
          "vendor": "Red Hat"
        },
        {
          "cpes": [
            "cpe:/a:redhat:openshift_ai:2.25::el9"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat OpenShift AI 2.25",
          "vendor": "Red Hat"
        },
        {
          "cpes": [
            "cpe:/a:redhat:openshift_ai:3.3::el9"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat OpenShift AI 3.3",
          "vendor": "Red Hat"
        },
        {
          "cpes": [
            "cpe:/a:redhat:openshift_ai:3.4::el9"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat OpenShift AI 3.4",
          "vendor": "Red Hat"
        },
        {
          "cpes": [
            "cpe:/a:redhat:ai_inference_server:3"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat AI Inference Server",
          "vendor": "Red Hat"
        },
        {
          "cpes": [
            "cpe:/a:redhat:enterprise_linux_ai:3"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat Enterprise Linux AI (RHEL AI) 3",
          "vendor": "Red Hat"
        },
        {
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "affected",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        }
      ],
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "F2E87BA6-DDF8-4FF6-A286-B44780082C69",
              "versionEndExcluding": "0.14.0",
              "versionStartIncluding": "0.10.1",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "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."
    },
    {
      "lang": "es",
      "value": "vLLM es un motor de inferencia y servicio para modelos de lenguaje grandes (LLM). A partir de la versi\u00f3n 0.10.1 y antes de la versi\u00f3n 0.14.0, vLLM carga m\u00f3dulos din\u00e1micos \u0027auto_map\u0027 de Hugging Face durante la resoluci\u00f3n del modelo sin depender de \u0027trust_remote_code\u0027, permitiendo que c\u00f3digo Python controlado por el atacante en un repositorio/ruta de modelo se ejecute al inicio del servidor. Un atacante que pueda influir en el repositorio/ruta del modelo (directorio local o repositorio remoto de Hugging Face) puede lograr la ejecuci\u00f3n de c\u00f3digo arbitrario en el host de vLLM durante la carga del modelo. Esto ocurre antes de cualquier manejo de solicitudes y no requiere acceso a la API. La versi\u00f3n 0.14.0 corrige el problema."
    }
  ],
  "id": "CVE-2026-22807",
  "lastModified": "2026-06-30T03:17:28.227",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.8,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 5.9,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 9.8,
          "baseSeverity": "CRITICAL",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.8,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 5.9,
        "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-22807",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-01-22T15:11:00.640100Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-01-21T22:15:49.077",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/vllm-project/vllm/commit/78d13ea9de4b1ce5e4d8a5af9738fea71fb024e5"
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    {
      "source": "security-advisories@github.com",
      "tags": [
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      ],
      "url": "https://github.com/vllm-project/vllm/pull/32194"
    },
    {
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      "tags": [
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        "Release Notes"
      ],
      "url": "https://github.com/vllm-project/vllm/releases/tag/v0.14.0"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch",
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      ],
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-2pc9-4j83-qjmr"
    },
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  "vulnStatus": "Modified",
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    }
  ]
}


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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.