Illuminating the Black Box of ML Systems
A novel approach to analyze the semantic flow within ML components
This research introduces semantic flow analysis as a method to understand the internal behaviors of ML-based systems that traditional testing can't capture.
- Develops techniques to track information flow through opaque ML components like DNNs and LLMs
- Bridges the gap between traditional software testing and ML system verification
- Enables deeper security analysis by revealing how information transforms within ML components
- Creates a foundation for more thorough reliability and safety assessment of ML-based systems
For security professionals, this approach offers crucial visibility into previously opaque ML components, enabling more comprehensive vulnerability detection and security verification in complex ML-integrated systems.