Lfprojects ≫ Mlflow

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset t...

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...

This vulnerability enables malicious users to read sensitive files on the server.

This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process.

A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.

A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it could be abuse to get a remote code execution on the victim machine.

with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.

Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2.