A new survey paper analyzes the rapidly growing field of LLM-driven penetration testing, identifying a lack of unified taxonomy and understanding of agent evolution. The research categorizes 81 papers from 2023-2026 into six areas, including evaluation benchmarks and general-purpose systems. It also traces a four-phase architectural evolution, noting that Reinforcement Learning with Verifiable Rewards (RLVR) has enabled agents to discover new attack strategies through self-improvement rather than just imitating expert demonstrations. AI
IMPACT Provides a structured overview of LLM applications in cybersecurity, highlighting research trends and future challenges.
RANK_REASON The cluster contains a survey paper analyzing a specific research area. [lever_c_demoted from research: ic=1 ai=1.0]
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