A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challengeshttps://arxiv.org/html/2607.02605...

A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challengeshttps://arxiv.org/html/2607.02605v1Agents4Pentest, an emerging class of LLM-based autonomous penetration testing systems, has become a rapidly growing area in security research. Despite this growth, the field still lacks a unified taxonomy, a systematic understanding of how agent architectures and evaluation benchmarks have co-evolved, and a clear characterization of remaining capability and reliability gaps. This survey addresses these gaps through a systematic analysis of 81 papers between 2023 and 2026. We organize the literature into six categories: evaluation benchmarks, general-purpose systems, domain-specific frameworks, CTF-based systems, defense-oriented research, and surveys. We further trace a four-phase architectural evolution from text-only reasoning agents to agents trained with Reinforcement Learning with Verifiable Rewards (RLVR), showing that each transition is driven by a distinct c...