mlflow-untrusted-dataset-xss-jfsa-2024-000631932.md

description	title	date_published	last_updated	xray_id	vul_id	cvss	severity	discovered_by	type
CVE-2024-27133, HIGH, Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.	MLflow untrusted dataset XSS	2024-02-23	2024-02-23	JFSA-2024-000631932	CVE-2024-27133		high	Uriya Yavnieli	vulnerability

Summary

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.

Component

mlflow

Affected versions

(,2.9.2]

Description

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 table fields.

PoC

The following mlflow code would be vulnerable to this issue, when using a Recipe that uses an untrusted dataset -

from mlflow.recipes import Recipe
from mlflow.pyfunc import PyFuncModel
from IPython.core.debugger import set_trace
regression_recipe = Recipe(profile="local")
# Run the full recipe
regression_recipe.run()
# Inspect the model training results
regression_recipe.inspect(step="train")
# Load the trained model
regression_model_recipe: PyFuncModel = regression_recipe.get_artifact("model")

Vulnerability Mitigations

No mitigations are supplied for this issue

References

Fix PR