Xiaoyun Zhang

RLShield: Dynamic Jailbreak Detection for LLMs via Reinforced Adaptive Learning

RLShield is a dynamic jailbreak detection framework that employs reinforcement learning for adaptive threshold selection and incorporates three key innovations: a dynamic retrieval and LLM-based rewriting module to simulate diverse adversarial contexts; a cross-layer representation analysis to pinpoint safety-critical parameters; and a Soft Actor-Critic based agent that learns to predict optimal, sample-specific detection thresholds.