The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks
This addresses the threat of LLM-driven cyberattacks for cybersecurity practitioners, presenting a novel defensive approach.
The paper tackles the problem of defending against cyberattacks powered by large language models (LLMs) by exploiting their inherent vulnerabilities, such as biases and memory limitations, achieving defense success rates of up to 90% in evaluations.
As large language models (LLMs) continue to evolve, their potential use in automating cyberattacks becomes increasingly likely. With capabilities such as reconnaissance, exploitation, and command execution, LLMs could soon become integral to autonomous cyber agents, capable of launching highly sophisticated attacks. In this paper, we introduce novel defense strategies that exploit the inherent vulnerabilities of attacking LLMs. By targeting weaknesses such as biases, trust in input, memory limitations, and their tunnel-vision approach to problem-solving, we develop techniques to mislead, delay, or neutralize these autonomous agents. We evaluate our defenses under black-box conditions, starting with single prompt-response scenarios and progressing to real-world tests using custom-built CTF machines. Our results show defense success rates of up to 90\%, demonstrating the effectiveness of turning LLM vulnerabilities into defensive strategies against LLM-driven cyber threats.