Ltrack
Monitors and analyzes potential security risks during the loading and execution of machine learning and artificial intelligence model files.
About
Ltrack is an open-source security observability framework for ML/AI model file loading, utilizing eBPF and Golang for low-overhead, high-performance monitoring. It helps identify vulnerabilities, unauthorized access, and anomalous activities in ML/AI workflows by focusing on critical system behaviors. Ltrack integrates with logging systems for real-time alerts and supports plugins for custom detectors, simplifying deployment with Golang's static binary packaging.
Key Features
- Monitors file operations in specified directories
- 33 GitHub stars
- Tracks execution of specified commands
- Monitors network activity on specific ports and protocols
- Offers flexible monitoring policy configuration via TOML files
- Supports log rotation, compression, and retention policies
Use Cases
- Meet regulatory requirements by enforcing strict access controls and audit trails.
- Enhance security in MLOps pipelines by auditing model deployment processes.
- Safeguard experimental models and datasets from unintended access or tampering.