RefPentester: A Knowledge-Informed Self-Reflective Penetration Testing Framework Based on Large Language Models (2505.07089v3)

Abstract: Automated penetration testing (AutoPT) powered by LLMs has gained attention for its ability to automate ethical hacking processes and identify vulnerabilities in target systems by leveraging the inherent knowledge of LLMs. However, existing LLM-based AutoPT frameworks often underperform compared to human experts in challenging tasks for several reasons: the imbalanced knowledge used in LLM training, short-sightedness in the planning process, and hallucinations during command generation. Moreover, the trial-and-error nature of the PT process is constrained by existing frameworks lacking mechanisms to learn from previous failures, restricting adaptive improvement of PT strategies. To address these limitations, we propose a knowledge-informed, self-reflective PT framework powered by LLMs, called RefPentester. This AutoPT framework is designed to assist human operators in identifying the current stage of the PT process, selecting appropriate tactics and techniques for each stage, choosing suggested actions, providing step-by-step operational guidance, and reflecting on and learning from previous failed operations. We also modeled the PT process as a seven-state Stage Machine to integrate the proposed framework effectively. The evaluation shows that RefPentester can successfully reveal credentials on Hack The Box's Sau machine, outperforming the baseline GPT-4o model by 16.7%. Across PT stages, RefPentester also demonstrates superior success rates on PT stage transitions.

RefPentester: A Knowledge-Informed Self-Reflective Penetration Testing Framework Based on LLMs

In the domain of cybersecurity, penetration testing (PT) remains a crucial method for identifying potential vulnerabilities within computer systems. The paper "RefPentester: A Knowledge-Informed Self-Reflective Penetration Testing Framework Based on LLMs" presents an advanced framework aimed at automating the PT process effectively using LLMs. This essay offers an expert analysis of the methodologies, results, and implications outlined in the paper.

Overview of Methodology

The RefPentester framework is designed to address notable deficiencies in existing LLM-based automated PT systems, such as short-sighted planning, hallucinations, and limited adaptability due to the lack of self-reflective mechanisms. To enhance these systems, RefPentester integrates a knowledge-informed approach where curated PT knowledge from authoritative resources is stored in a Vector Database (VDB). This knowledge is utilized via a Retrieval-Augmented Generation (RAG) pipeline to provide precise guidance throughout the PT stages.

RefPentester is structured into five key components:

1. Process Navigator employs a seven-state PT Stage Machine to ascertain the current PT stage, facilitating informed decisions on tactics and techniques.
2. Generator produces detailed PT instructions, leveraging insights from the Process Navigator to guide human operators through the execution process.
3. Reflector evaluates PT actions and outcomes, applying reinforcement mechanisms to learn from failed operations.
4. Success Log serves as a repository of successful PT experiences, preventing context loss.
5. Failure Log records unsuccessful attempts, enabling continuous learning and strategy refinement.

This framework operates within a human-in-the-loop paradigm, ensuring that generated PT actions are executed and analyzed in real-time, allowing for dynamic adaptation and improvement.

Evaluation and Results

Through rigorous testing environments, specifically the Hack The Box platform, RefPentester demonstrated substantial improvements over baseline models, notably GPT-4o. Noteworthy results include:

• A 16.7% improvement in credential capture rate, indicating enhanced efficiency in PT execution.
• Superior PT stage transition success rates, thereby demonstrating robustness across varied PT scenarios.

These results underscore the efficacy of integrating structured PT knowledge and reflective learning mechanisms within LLMs for automated penetration testing. RefPentester achieves this by effectively mitigating common pitfalls such as hallucinations and knowledge imbalance, which often hinder LLM performance in specialized domains.

Implications and Future Directions

The research presented in this paper holds tangible implications for both theoretical advancements and practical applications in cybersecurity. From a theoretical perspective, RefPentester contributes to the ongoing discourse on the integration of human knowledge into machine learning systems, advocating for mechanisms that enable continuous learning and adaptation within AI frameworks.

Practically, RefPentester's approach suggests a viable pathway for deploying more effective automated PT systems, particularly as cybersecurity threats continue to evolve. Such systems can streamline the PT process, reducing the need for extensive human oversight while enhancing the identification and mitigation of vulnerabilities.

Looking forward, the paper highlights potential avenues for further research into dynamic knowledge integration pipelines and reinforcement learning models that incorporate human feedback. By investing in these areas, the development of scalable and adaptive PT solutions capable of handling increasingly sophisticated cybersecurity challenges becomes feasible.

In conclusion, the RefPentester framework presents a compelling advance in automated penetration testing, leveraging the intrinsic capabilities of LLMs through structured and self-reflective methodologies. This research sets the stage for future innovations that can better safeguard digital environments in the face of growing threats.