CVE-2025-11200
📋 TL;DR
This vulnerability allows remote attackers to bypass authentication in MLflow installations due to weak password requirements. Attackers can gain unauthorized access without credentials. All MLflow deployments with the vulnerable code are affected.
💻 Affected Systems
- MLflow
📦 What is this software?
Mlflow by Lfprojects
⚠️ Risk & Real-World Impact
Worst Case
Complete compromise of MLflow instance leading to data theft, model manipulation, and lateral movement to connected systems.
Likely Case
Unauthorized access to MLflow dashboard, exposure of sensitive ML metadata, and potential data exfiltration.
If Mitigated
Limited impact if strong network segmentation and additional authentication layers are in place.
🎯 Exploit Status
Authentication bypass vulnerabilities are typically easy to exploit once details are known.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: Commit 1f74f3f24d8273927b8db392c23e108576936c54 or later
Vendor Advisory: https://github.com/mlflow/mlflow/commit/1f74f3f24d8273927b8db392c23e108576936c54
Restart Required: Yes
Instructions:
1. Update MLflow to latest version or apply commit 1f74f3f24d8273927b8db392c23e108576936c54
2. Restart MLflow service
3. Verify authentication is working properly
🔧 Temporary Workarounds
Implement external authentication proxy
allPlace MLflow behind a reverse proxy with strong authentication (OAuth, SAML, etc.)
Network isolation
linuxRestrict access to MLflow via firewall rules to trusted IPs only
iptables -A INPUT -p tcp --dport 5000 -s trusted_ip -j ACCEPT
iptables -A INPUT -p tcp --dport 5000 -j DROP
🧯 If You Can't Patch
- Implement strict network access controls to limit MLflow exposure
- Deploy Web Application Firewall (WAF) with authentication bypass protection rules
🔍 How to Verify
Check if Vulnerable:
Check MLflow version/git commit. If before commit 1f74f3f24d8273927b8db392c23e108576936c54, it's vulnerable.Check Version:
mlflow --versionVerify Fix Applied:
Attempt authentication bypass using known weak passwords. Should fail after patch.📡 Detection & Monitoring
Log Indicators:
- Failed authentication attempts with weak passwords
- Successful logins from unexpected IPs
- Authentication bypass patterns
Network Indicators:
- Unusual authentication traffic patterns
- Access to MLflow endpoints without proper auth headers
SIEM Query:
source="mlflow.log" AND (event="authentication_failure" OR event="authentication_bypass")