CVE-2024-27133
📋 TL;DR
CVE-2024-27133 is a cross-site scripting (XSS) vulnerability in MLflow that occurs when running recipes with untrusted datasets. Insufficient sanitization of dataset table fields allows attackers to inject malicious scripts, which can lead to client-side remote code execution when recipes are executed in Jupyter Notebook. This affects MLflow users who process untrusted datasets through recipes.
💻 Affected Systems
- MLflow
📦 What is this software?
Mlflow by Lfprojects
⚠️ Risk & Real-World Impact
Worst Case
Full client-side remote code execution in Jupyter Notebook environment, potentially compromising the user's system and allowing lateral movement within the network.
Likely Case
Cross-site scripting attacks leading to session hijacking, credential theft, or unauthorized actions within the MLflow interface.
If Mitigated
Limited impact with proper input validation and output encoding, potentially only affecting UI elements without code execution.
🎯 Exploit Status
Exploitation requires user interaction with malicious datasets. Public proof-of-concept exists in research publications.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: 2.12.1
Vendor Advisory: https://github.com/mlflow/mlflow/pull/10893
Restart Required: No
Instructions:
1. Upgrade MLflow to version 2.12.1 or later using pip: pip install --upgrade mlflow>=2.12.1
2. Verify the upgrade completed successfully
3. No service restart required for MLflow itself, but restart any dependent services
🔧 Temporary Workarounds
Input Validation for Datasets
allImplement strict input validation and sanitization for all dataset fields before processing in MLflow recipes
# Implement custom dataset validation logic
# Example Python snippet:
import html
def sanitize_dataset_field(field):
return html.escape(str(field))
Disable Jupyter Notebook Integration
linuxTemporarily disable Jupyter Notebook execution for MLflow recipes to prevent RCE escalation
# Set environment variable to disable notebook execution
export MLFLOW_DISABLE_NOTEBOOK_EXECUTION=true
🧯 If You Can't Patch
- Implement strict dataset source validation - only use trusted, verified datasets in MLflow recipes
- Enable Content Security Policy (CSP) headers to mitigate XSS impact and prevent script execution
🔍 How to Verify
Check if Vulnerable:
Check MLflow version: python -c "import mlflow; print(mlflow.__version__)" - if version is less than 2.12.1, system is vulnerableCheck Version:
python -c "import mlflow; print('MLflow version:', mlflow.__version__)"Verify Fix Applied:
After upgrade, verify version is 2.12.1 or higher and test dataset processing with known safe payloads📡 Detection & Monitoring
Log Indicators:
- Unusual dataset processing patterns
- Multiple failed dataset validations
- Suspicious script tags in dataset fields
Network Indicators:
- Unexpected outbound connections from MLflow servers
- Unusual data transfers to/from dataset sources
SIEM Query:
source="mlflow" AND (dataset_processing_error OR suspicious_dataset_content)