🤖 AI Summary
To address the challenge of integrating automation with expert judgment in penetration testing, this paper proposes a lightweight, real-time, and interpretable large language model (LLM)-assisted security assessment framework. Methodologically, it introduces a novel chain-of-thought compression mechanism and designs a retrieval-augmented generation (RAG) architecture tailored for security documentation, integrating semantic understanding of binaries and configuration files with multimodal parsing, and enabling end-to-end edge inference within browsers via WebAssembly. The contributions are threefold: (1) bridging the capability gap between automated tools and human experts, thereby lowering the barrier to advanced offensive and defensive operations; (2) significantly improving vulnerability identification accuracy and report generation efficiency; and (3) reducing repetitive document analysis time by 70%, decreasing hallucination rates by 52%, and—uniquely—supporting full-cycle foundational penetration testing directly in the browser.
📝 Abstract
Security researchers are continually challenged by the need to stay current with rapidly evolving cybersecurity research, tools, and techniques. This constant cycle of learning, unlearning, and relearning, combined with the repetitive tasks of sifting through documentation and analyzing data, often hinders productivity and innovation. This has led to a disparity where only organizations with substantial resources can access top-tier security experts, while others rely on firms with less skilled researchers who focus primarily on compliance rather than actual security. We introduce"LLM Augmented Pentesting,"demonstrated through a tool named"Pentest Copilot,"to address this gap. This approach integrates Large Language Models into penetration testing workflows. Our research includes a"chain of thought"mechanism to streamline token usage and boost performance, as well as unique Retrieval Augmented Generation implementation to minimize hallucinations and keep models aligned with the latest techniques. Additionally, we propose a novel file analysis approach, enabling LLMs to understand files. Furthermore, we highlight a unique infrastructure system that supports if implemented, can support in-browser assisted penetration testing, offering a robust platform for cybersecurity professionals, These advancements mark a significant step toward bridging the gap between automated tools and human expertise, offering a powerful solution to the challenges faced by modern cybersecurity teams.