Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction
ICML 2026 2026
This work improves behavioral safety for LLM agents by correcting risky thoughts before action execution.
ICML 2026 2026
This work improves behavioral safety for LLM agents by correcting risky thoughts before action execution.
USENIX Security 2026 2026
MATE is a policy-aware security auditing framework for mobile agents, using synthesis-driven trajectory learning to find unsafe behaviors.
ACM CCS 2026 2026
MirrorGuard improves the security of computer-use agents by using simulation-to-real reasoning correction.
arXiv preprint 2024
This work studies knowledge-base extraction attacks against retrieval-augmented LLM applications and introduces a feedback-guided extraction method.