LuaN1ao is an autonomous penetration testing agent that leverages large language models to simulate expert security researcher behavior. It implements a P-E-R (Planner-Executor-Reflector) architecture combined with causal graph reasoning to dynamically plan attack paths, build evidence-driven hypotheses, and learn from failed attempts. The system aims to overcome limitations of traditional rule-based scanning tools by reasoning about complex real-world security scenarios.