CVE-2025-11200: Lfprojects MLflow Auth Bypass Vulnerability

CVE-2025-11200 Overview

CVE-2025-11200 is a critical authentication bypass vulnerability in MLflow, the popular open-source platform for managing the machine learning lifecycle. The vulnerability exists due to weak password requirements in the handling of passwords, allowing remote attackers to bypass authentication on affected MLflow installations without any prior authentication.

This vulnerability was identified through the Zero Day Initiative as ZDI-CAN-26916 and subsequently published as ZDI-25-932. The flaw enables attackers to potentially gain unauthorized access to MLflow deployments, compromising the integrity and confidentiality of machine learning experiments, models, and associated data.

Critical Impact

Remote unauthenticated attackers can bypass authentication mechanisms due to insufficient password strength requirements, potentially gaining full access to MLflow systems and sensitive ML artifacts.

Affected Products

• lfprojects mlflow (all versions prior to patch)

Discovery Timeline

• 2025-10-29 - CVE-2025-11200 published to NVD
• 2025-12-31 - Last updated in NVD database

Technical Details for CVE-2025-11200

Vulnerability Analysis

This vulnerability falls under CWE-521 (Weak Password Requirements), a configuration and design flaw that compromises the security posture of authentication mechanisms. The fundamental issue lies in MLflow's insufficient enforcement of password complexity rules, which allows users to set weak, easily guessable passwords.

The vulnerability is particularly severe because it can be exploited remotely over the network without requiring any prior authentication or user interaction. An attacker who successfully exploits this flaw can bypass authentication entirely, gaining unauthorized access to the MLflow system with potentially full privileges over machine learning experiments, models, and data pipelines.

Root Cause

The root cause of CVE-2025-11200 is the inadequate minimum password length requirement in MLflow's authentication system. The original implementation allowed passwords as short as 4 characters, which is far below industry security standards and makes accounts susceptible to brute-force attacks, dictionary attacks, and credential stuffing.

Additionally, the default admin password was set to the trivially weak value password, creating an obvious security risk for deployments that did not explicitly change this credential during setup.

Attack Vector

The attack can be executed remotely over the network. An attacker can exploit this vulnerability through several methods:

1. Brute-force attacks - With a minimum password length of only 4 characters, the keyspace is small enough to be exhaustively searched
2. Dictionary attacks - Common passwords and word combinations can be tested rapidly
3. Default credential exploitation - Deployments using the default admin:password credentials are immediately compromised
4. Credential stuffing - Weak password policies mean users are more likely to reuse passwords from other breached services

The following patches from the MLflow security commit demonstrate the vulnerability and its fix:

HTML Form Validation Patch:

   <br>
   <label for="password">Password:</label>
   <br>
-  <input type="password" id="password" name="password" minlength="4">
+  <input type="password" id="password" name="password" minlength="12">
   <br>
   <br>
   <input type="submit" value="Sign up">

Source: MLflow GitHub Commit

Default Admin Configuration Patch:

 default_permission = READ
 database_uri = sqlite:///basic_auth.db
 admin_username = admin
-admin_password = password
+admin_password = password1234
 authorization_function = mlflow.server.auth:authenticate_request_basic_auth

Source: MLflow GitHub Commit

Detection Methods for CVE-2025-11200

Indicators of Compromise

• Unusual login patterns or multiple failed authentication attempts from single IP addresses
• Successful logins from unexpected geographic locations or IP ranges
• Access to MLflow experiments or models from unauthorized user accounts
• Audit logs showing authentication events with default or previously compromised credentials

Detection Strategies

• Monitor authentication logs for brute-force attack patterns (high volume of failed login attempts)
• Implement rate limiting on authentication endpoints and alert on threshold violations
• Audit user accounts for passwords that do not meet the new 12-character minimum requirement
• Deploy web application firewalls (WAF) with rules to detect credential stuffing attempts

Monitoring Recommendations

• Enable verbose logging for MLflow authentication events and forward to SIEM solutions
• Set up alerts for successful authentications following multiple failed attempts
• Monitor for access patterns inconsistent with normal ML workflow behaviors
• Regularly review and audit user account credentials for compliance with password policies

How to Mitigate CVE-2025-11200

Immediate Actions Required

• Update MLflow to the latest patched version containing commit 1f74f3f24d8273927b8db392c23e108576936c54
• Immediately change the default admin password to a strong, unique password of at least 12 characters
• Audit all existing user accounts and force password resets for accounts with weak passwords
• Review access logs for signs of unauthorized access or suspicious authentication activity

Patch Information

LF Projects has released a security patch addressing this vulnerability. The fix is available in the MLflow GitHub repository commit 1f74f3f24d8273927b8db392c23e108576936c54. This patch increases the minimum password length from 4 characters to 12 characters and updates the default admin password.

Organizations should update their MLflow installations to include this security fix as soon as possible. For detailed technical information, consult the Zero Day Initiative Advisory ZDI-25-932.

Workarounds

• Implement network segmentation to restrict access to MLflow instances from trusted networks only
• Deploy a reverse proxy with strong authentication mechanisms (e.g., OAuth, SAML) in front of MLflow
• Use firewall rules to limit access to MLflow ports from authorized IP addresses
• Enable multi-factor authentication if supported by your MLflow deployment configuration

# Example: Restrict MLflow access using iptables
# Allow MLflow access only from trusted network
iptables -A INPUT -p tcp --dport 5000 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 5000 -j DROP

# Update MLflow configuration with stronger password requirements
# Edit basic_auth.ini to set a strong admin password
admin_password = YourStr0ng!P@ssw0rd#Here