DMRL: Data- and Model-Aware Reward Learning for Data Extraction

Large language models LLMs are inherently vulnerable to unintended privacy breaches. Consequently, systematic red-teaming research is essential for developing robust defense mechanisms. However, current data extraction methods suffer from several limitations: 1 rely on dataset duplicates...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...