CVE-2026-116692
Received Received - Intake

Remote Code Execution in MLflow AI Platform

Vulnerability report for CVE-2026-116692, including description, CVSS score, EPSS score, affected products, exploitability, helpful resources, and attack-flow context.

Publication date: 2026-10-27

Last updated on: 2026-10-27

Assigner: GitHub, Inc.

Description

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, third-party scorer deserialization in mlflow/genai/scorers/base.py accepts request-selected descendants of THIRD_PARTY_SCORER_ALLOWED_MODULES rather than requiring an exact allowed module. A caller can use MLflow artifact endpoints to place a Python module beneath a writable allowed package and submit its module and class through /ajax-api/3.0/mlflow/scorer/invoke. Scorer.model_validate_json() imports the module and constructs the class before trace processing. Authentication-disabled deployments permit this code execution without credentials, while basic-auth deployments require a principal capable of creating an experiment and run and uploading artifacts. In either configuration, the allowed scorer package must be writable by the server process. This issue is fixed in version 3.17.0.

CVSS Scores

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Meta Information

Published
2026-10-27
Last Modified
2026-10-27
Generated
2026-10-27
AI Q&A
2026-10-27
EPSS Evaluated
N/A
NVD
EUVD

Affected Vendors & Products

Showing 1 associated CPE
Vendor Product Version / Range
mlflow mlflow >= 3.12.0rc0, < 3.17.0

Helpful Resources

Exploitability

CWE
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KEV
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CWE ID Description
CWE-470 The product uses external input with reflection to select which classes or code to use, but it does not sufficiently prevent the input from selecting improper classes or code.
CWE-94 The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

Attack-Flow Graph

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Executive Summary

This vulnerability in MLflow (versions 3.12.0rc0 to 3.17.0) allows third-party scorer deserialization to accept request-selected descendants of allowed modules instead of exact matches. Attackers can exploit this by placing a Python module under a writable allowed package and submitting it via MLflow artifact endpoints to execute arbitrary code.

Detection Guidance

Check MLflow server version for versions between 3.12.0rc0 and 3.17.0. Inspect server logs for unauthorized scorer module imports or artifact uploads. Verify if MLflow artifact endpoints allow untrusted module uploads under allowed packages.

Impact Analysis

Unauthenticated attackers can execute arbitrary code on systems running vulnerable MLflow deployments. This could lead to data breaches, unauthorized access, or system compromise. Authentication-enabled deployments require an attacker to have experiment/run creation privileges and artifact upload access.

Compliance Impact

This vulnerability could lead to unauthorized data access or exfiltration, violating GDPR's data protection principles and HIPAA's security requirements. Organizations using vulnerable MLflow versions may face compliance violations, legal penalties, and reputational damage.

Mitigation Strategies

Upgrade MLflow to version 3.17.0 or later. Disable authentication-disabled deployments. Restrict write access to allowed scorer packages. Review and audit artifact uploads and scorer configurations.

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