Chameleon: A Deception Defense Strategy Against LLM-Assisted Attacker in New Power Systems

Abstract

As new power systems become increasingly dependent on cloud-supported cyber-physical systems, their openness and interconnectivity continue to increase, thereby exposing risk points for advanced persistent threats (APTs). Deception defense has been widely regarded as an effective proactive approach for mitigating APT threats. However, the remarkable reasoning capabilities of large language models (LLMs) have enabled APT attackers to leverage LLM-based semantic understanding and task-planning capabilities to conduct automated, intelligent penetration attacks, while also bringing new challenges for traditional deception defense mechanisms. To address this issue, we propose a Chameleon service mechanism that constructs multiple types of LLM-oriented deceptive services based on the shared characteristics that LLMs exhibit during environment reconnaissance and target screening, and further incorporates an attack-defense game model with Minimax Q-learning for deployment. In this way, the proposed method increases the likelihood of trapping attackers while minimizing interference with normal power operations. The experimental results show that the proposed Chameleon service mechanism can effectively enhance the trapping effect of deceptive services on LLM-assisted attackers and demonstrate good effectiveness and stability across different candidate scales and LLM evaluation conditions. Our method can provide a feasible solution for proactive deception defense against intelligent attackers in new power systems.