LLMs and Security: MRJ-Agent for a Multi-Round Attack
The episode introduces MRJ-Agent, an innovative multi-round attack agent for Large Language Models (LLMs). Unlike existing single-round attacks, MRJ-Agent simulates complex human interactions by employing risk decomposition strategies and psychological induction to prompt LLMs into generating harmful responses. The findings demonstrate a high success rate across various models, including GPT-4 and LLaMA2-7B, highlighting the susceptibility of LLMs to multi-round attacks and the pressing need for more robust defenses. The research outlines future implications for the security and alignment of LLMs, emphasizing the importance of adopting a proactive and adaptive approach to enhance resilience.