Pen-Strategist: A Reasoning Framework for Penetration Testing Strategy Formation and Analysis
Yasod Ginige, Pasindu Marasinghe, Sajal Jain, Suranga Seneviratne

TL;DR
Pen-Strategist introduces a novel reasoning framework that enhances penetration testing strategies through logical reasoning, domain-specific models, and improved action prediction, significantly outperforming existing methods.
Contribution
It presents a new domain-specific reasoning model and classifier for strategy formulation and action selection in penetration testing, with substantial performance improvements.
Findings
87% improvement in strategy derivation performance
47.5% enhancement in subtask completion in automated pentesting
18% performance gain on CTFKnow benchmark
Abstract
Cyber threats are rapidly increasing, expanding their impact from large-scale enterprises to government services and individual users, making robust security systems increasingly essential. However, a significant shortage of skilled cybersecurity professionals exacerbates this challenge. While recent research has explored automating tasks such as penetration testing using LLM-based agents, existing frameworks often perform poorly due to limited capability in strategy formulation, domain-specific reasoning, and accurate action and tool selection. To overcome these limitations, we propose Pen-Strategist framework, consisting of a novel domain-specific reasoning model that derives pentesting strategies via logical reasoning and a classifier that converts the strategies into actionable steps. First, we construct a reasoning dataset containing logical explanations for both strategy…
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