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.
Published: 2026-10-27
Score: 9.8 Critical
EPSS: n/a
KEV: No
AI Analysis

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Remediation

No vendor fix or workaround currently provided.

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Tracking

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Advisories
Source ID Title
Github GHSA Github GHSA GHSA-26p8-2jq9-3vq9 MLflow vulnerable to remote code execution on unauthenticated servers via third-party scorer deserialization importing caller-placed artifact modules
History

Tue, 27 Oct 2026 22:00:00 +0000

Type Values Removed Values Added
First Time appeared Mlflow
Mlflow mlflow
Vendors & Products Mlflow
Mlflow mlflow

Tue, 27 Oct 2026 20:30:00 +0000

Type Values Removed Values Added
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.
Title MLflow: Remote code execution on unauthenticated MLflow servers via third-party scorer deserialization importing caller-placed artifact modules
Weaknesses CWE-470
CWE-94
References
Metrics cvssV3_1

{'score': 9.8, 'vector': 'CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H'}


cve-icon MITRE

Status: PUBLISHED

Assigner: GitHub_M

Published:

Updated: 2026-10-27T20:03:49.661Z

Reserved: 2026-10-26T20:00:59.830Z

Link: CVE-2026-116692

cve-icon Vulnrichment

No data.

cve-icon NVD

Status : Received

Published: 2026-10-27T21:18:05.013

Modified: 2026-10-27T21:18:05.013

Link: CVE-2026-116692

cve-icon Redhat

No data.

cve-icon OpenCVE Enrichment

Updated: 2026-10-27T21:45:08Z

Weaknesses
  • CWE-470

    Use of Externally-Controlled Input to Select Classes or Code ('Unsafe Reflection')

  • CWE-94

    Improper Control of Generation of Code ('Code Injection')