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.
Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing
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CyberEvolver introduces a four-layer self-evolving agent architecture with trace-to-diagnosis and population beam search that raises seed agent success rates by 13.6% on CTF, exploitation, and penetration tasks across four LLMs.
An evaluation protocol for AI pentesting agents that scores validated vulnerability discovery using LLM-based semantic matching and bipartite resolution.
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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CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly
CyberEvolver introduces a four-layer self-evolving agent architecture with trace-to-diagnosis and population beam search that raises seed agent success rates by 13.6% on CTF, exploitation, and penetration tasks across four LLMs.
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From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World
An evaluation protocol for AI pentesting agents that scores validated vulnerability discovery using LLM-based semantic matching and bipartite resolution.