FKIE_CVE-2026-2651
Vulnerability from fkie_nvd - Published: 2026-05-25 07:16 - Updated: 2026-07-21 19:10
Severity
Summary
A vulnerability in MLflow versions <=3.10.1.dev0 allows unauthorized access to multipart upload (MPU) endpoints when the `--serve-artifacts` mode is enabled. The authorization logic does not enforce resource-level permission checks for `/mlflow-artifacts/mpu/*` endpoints, enabling attackers to overwrite artifacts belonging to other users. This can lead to unauthorized cross-user writes, model supply chain poisoning, and arbitrary code execution when compromised models are loaded. The issue is resolved in version 3.10.0.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| lfprojects | mlflow | * |
{
"affected": [
{
"affectedData": [
{
"product": "mlflow/mlflow",
"vendor": "mlflow",
"versions": [
{
"lessThan": "3.10.0",
"status": "affected",
"version": "unspecified",
"versionType": "custom"
}
]
}
],
"source": "security@huntr.dev"
},
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],
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"packageName": "rhoai/odh-mlflow-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
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"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
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"cpes": [
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],
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"packageName": "rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
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},
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],
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"packageName": "rhoai/odh-th06-cpu-torch210-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
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"cpes": [
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"packageName": "rhoai/odh-th06-rocm64-torch291-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
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"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-training-cuda128-torch29-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
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"cpes": [
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],
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"product": "Red Hat OpenShift AI (RHOAI)",
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},
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],
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"packageName": "rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
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"cpes": [
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],
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"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
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"cpes": [
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],
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"packageName": "rhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
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"cpes": [
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],
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"packageName": "rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
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"cpes": [
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],
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"packageName": "rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
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],
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"vendor": "Red Hat"
}
],
"source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*",
"matchCriteriaId": "C1C49CD5-5BB0-422B-9A71-5A6832DF6713",
"versionEndIncluding": "3.10.1",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
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],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A vulnerability in MLflow versions \u003c=3.10.1.dev0 allows unauthorized access to multipart upload (MPU) endpoints when the `--serve-artifacts` mode is enabled. The authorization logic does not enforce resource-level permission checks for `/mlflow-artifacts/mpu/*` endpoints, enabling attackers to overwrite artifacts belonging to other users. This can lead to unauthorized cross-user writes, model supply chain poisoning, and arbitrary code execution when compromised models are loaded. The issue is resolved in version 3.10.0."
},
{
"lang": "es",
"value": "Una vulnerabilidad en las versiones de MLflow =3.10.1.dev0 permite el acceso no autorizado a los puntos finales de carga multiparte (MPU) cuando el modo \u0027--serve-artifacts\u0027 est\u00e1 habilitado. La l\u00f3gica de autorizaci\u00f3n no aplica comprobaciones de permisos a nivel de recurso para los puntos finales \u0027/mlflow-artifacts/mpu/ \u0027, lo que permite a los atacantes sobrescribir artefactos pertenecientes a otros usuarios. Esto puede conducir a escrituras no autorizadas entre usuarios, envenenamiento de la cadena de suministro de modelos y ejecuci\u00f3n de c\u00f3digo arbitrario cuando se cargan modelos comprometidos. El problema est\u00e1 resuelto en la versi\u00f3n 3.10.0."
}
],
"id": "CVE-2026-2651",
"lastModified": "2026-07-21T19:10:00.107",
"metrics": {
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"baseScore": 9.0,
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H",
"version": "3.0"
},
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"impactScore": 6.0,
"source": "security@huntr.dev",
"type": "Secondary"
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{
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"role": "CISA Coordinator",
"timestamp": "2026-05-26T00:00:00+00:00",
"version": "2.0.3"
}
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},
"published": "2026-05-25T07:16:15.100",
"references": [
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"tags": [
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"url": "https://github.com/mlflow/mlflow/commit/d7290811d8f3c95366d80109424edc1fb1ad966f"
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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
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
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