CVE-2024-3660
📋 TL;DR
This CVE describes a critical arbitrary code injection vulnerability in TensorFlow's Keras framework that allows attackers to execute arbitrary code with the same permissions as the vulnerable application. Attackers can exploit this by providing a malicious model that triggers code execution regardless of the application's intended functionality. Any system running TensorFlow with Keras below version 2.13 is affected.
💻 Affected Systems
- TensorFlow with Keras framework
📦 What is this software?
Keras by Keras
⚠️ Risk & Real-World Impact
Worst Case
Complete system compromise leading to data theft, ransomware deployment, lateral movement across networks, and persistent backdoor installation.
Likely Case
Remote code execution leading to data exfiltration, cryptocurrency mining, or botnet enrollment of vulnerable systems.
If Mitigated
Limited impact through network segmentation and strict input validation, potentially preventing successful exploitation.
🎯 Exploit Status
Exploitation requires loading a malicious model file, which can be delivered through various vectors including compromised repositories or user uploads.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.13 and later
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories
Restart Required: Yes
Instructions:
1. Identify all systems running TensorFlow < 2.13
2. Backup current models and configurations
3. Update TensorFlow to version 2.13 or later using pip: pip install --upgrade tensorflow
4. Restart all applications using TensorFlow
5. Validate that applications function correctly with updated version
🔧 Temporary Workarounds
Model Validation and Sanitization
allImplement strict validation of model files before loading, including checks for suspicious patterns and digital signatures.
Network Segmentation
allIsolate TensorFlow applications from sensitive networks and implement strict firewall rules.
🧯 If You Can't Patch
- Implement strict input validation for all model loading operations
- Run TensorFlow applications with minimal privileges and in isolated containers
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c "import tensorflow as tf; print(tf.__version__)" and verify if < 2.13Check Version:
python -c "import tensorflow as tf; print(tf.__version__)"Verify Fix Applied:
After update, run: python -c "import tensorflow as tf; print(tf.__version__)" and confirm version >= 2.13📡 Detection & Monitoring
Log Indicators:
- Unexpected model loading from untrusted sources
- Process spawning from TensorFlow applications
- Memory allocation patterns indicative of code injection
Network Indicators:
- Outbound connections from TensorFlow applications to suspicious IPs
- Unexpected data exfiltration patterns
SIEM Query:
source="tensorflow" AND (event="model_load" OR event="process_spawn") AND NOT source_ip IN trusted_ips