Package Instance

MLflow is vulnerable to an authorization bypass affecting the AJAX endpoint used to download saved model artifacts. Due to missing access‑control validation, a user without permissions to a given experiment can directly query this endpoint and retrieve model artifacts they are not authorized to access.

 
This issue affects MLflow version through 3.10.1

Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe.

This issue leads to a client-side RCE when running an untrusted recipe in Jupyter Notebook.

The vulnerability stems from lack of sanitization over template variables.

MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim. 

This issue affects MLflow version through 3.10.1

Information exposure in MLflow
An issue in MLFlow versions 2.8.1 and before allows a remote attacker to obtain sensitive information via a crafted request to REST API.