CVE-2026-69148

Received Received - Intake

Path Traversal in MLflow AI Platform

Publication date: 2026-08-17

Last updated on: 2026-08-17

Assigner: GitHub, Inc.

Description

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Prior to 3.15.0, CreateModelVersion accepts a run_id or model_id after _validate_source_run() or _validate_source_model() in mlflow/server/handlers.py verifies only path containment, allowing authenticated users to create a model version that references another user's artifact directory and read files through GET /model-versions/get-artifact without the required READ permission. This issue is fixed in version 3.15.0.

CVSS Scores

EPSS Scores

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Meta Information

Published

2026-08-17

Last Modified

2026-08-17

Generated

2026-08-18

AI Q&A

2026-08-18

EPSS Evaluated

N/A

NVD

CVE-2026-69148

Affected Vendors & Products

Vendor	Product	Version / Range
mlflow	mlflow	3.15.0

Helpful Resources

Exploitability

CWE

KEV

CWE ID	Description
CWE-862	The product does not perform an authorization check when an actor attempts to access a resource or perform an action.

Attack-Flow Graph

Executive Summary

This vulnerability in MLflow versions before 3.15.0 allows authenticated users to create a model version that references another user's artifact directory. The system only checks path containment but does not verify proper permissions, enabling unauthorized access to read files via the GET /model-versions/get-artifact endpoint.

Detection Guidance

To detect this vulnerability, check if your MLflow instance is running a version prior to 3.15.0. Verify the installed version using commands like 'pip show mlflow' or 'mlflow --version'. If the version is below 3.15.0, the system is vulnerable.

Impact Analysis

An attacker with access could read sensitive files from other users' artifact directories, potentially exposing confidential data such as model weights, training datasets, or other proprietary information stored in MLflow.

Compliance Impact

This vulnerability could lead to unauthorized data access, violating confidentiality requirements in GDPR and HIPAA. It may result in data breaches, non-compliance with access controls, and potential legal penalties due to exposure of protected health or personal data.

Mitigation Strategies

Upgrade MLflow to version 3.15.0 or later to address the vulnerability. Ensure all users have appropriate permissions and review artifact directory access controls.

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