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The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks

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arxiv 2410.15396 v1 pith:HN5ULNQQ submitted 2024-10-20 cs.CR cs.AI

classification cs.CRcs.AI
keywords defensellmsagentsautonomouscybercyberattacksstrategiesvulnerabilities
verification ladder T0 review T1 audit T2 compute T3 formal

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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

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

    cs.CR 2026-08 conditional novelty 5.0 of 10

    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.

  2. Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks

    cs.NI 2025-05 conditional novelty 4.0 of 10

    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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