The first SoK on LLM-based AutoPT frameworks provides a six-dimension taxonomy of agent designs and a unified empirical benchmark evaluating 15 frameworks via over 10 billion tokens and 1,500 manually reviewed logs.
Automated penetration testing with llm agents and classical planning
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Empirical study of 400 LLM attack runs finds exploitation success rates of 25-85% across four models against a fixed multi-service honeypot, with model-distinctive failure modes and p<0.001 differences.
Automation-Exploit is a multi-agent LLM system that uses conditional digital-twin validation to perform risk-mitigated exploitation of logical, web, and memory-corruption vulnerabilities in black-box targets.
citing papers explorer
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Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing
The first SoK on LLM-based AutoPT frameworks provides a six-dimension taxonomy of agent designs and a unified empirical benchmark evaluating 15 frameworks via over 10 billion tokens and 1,500 manually reviewed logs.
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How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency
Empirical study of 400 LLM attack runs finds exploitation success rates of 25-85% across four models against a fixed multi-service honeypot, with model-distinctive failure modes and p<0.001 differences.
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Automation-Exploit: A Multi-Agent LLM Framework for Adaptive Offensive Security with Digital Twin-Based Risk-Mitigated Exploitation
Automation-Exploit is a multi-agent LLM system that uses conditional digital-twin validation to perform risk-mitigated exploitation of logical, web, and memory-corruption vulnerabilities in black-box targets.