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The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks
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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.
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Cited by 2 Pith papers
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On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models
A PRISMA-based survey of 85 papers shows agentic LLM security research is attack-heavy and perception-focused, leaving action-layer and code-execution risks understudied.
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Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks
A review of LLM-based agents as autonomous cyberattackers, arguing that they lower attack costs, scale up threats, and outpace existing defenses.
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