This paper addresses the prevalent lack of tools to facilitate and empower Game Theory and Artificial Intelligence (AI) research in cybersecurity. The primary contribution is the introduction of ExploitFlow (EF), an AI and Game Theory-driven modular library designed for cyber security exploitation. EF aims to automate attacks, combining exploits from various sources, and capturing system states post-action to reason about them and understand potential attack trees. The motivation behind EF is to bolster Game Theory and AI research in cybersecurity, with robotics as the initial focus. Results indicate that EF is effective for exploring machine learning in robot cybersecurity. An artificial agent powered by EF, using Reinforcement Learning, outperformed both brute-force and human expert approaches, laying the path for using ExploitFlow for further research. Nonetheless, we identified several limitations in EF-driven agents, including a propensity to overfit, the scarcity and production cost of datasets for generalization, and challenges in interpreting networking states across varied security settings. To leverage the strengths of ExploitFlow while addressing identified shortcomings, we present Malism, our vision for a comprehensive automated penetration testing framework with ExploitFlow at its core.

翻译：本文针对网络安全领域博弈论与人工智能研究中普遍缺乏辅助工具的问题，提出主要贡献——ExploitFlow（EF），一个由人工智能与博弈论驱动的模块化网络安全利用库。EF旨在自动化攻击流程，整合多源漏洞利用程序，并在动作执行后捕获系统状态以进行推理分析，从而理解潜在攻击树。其研发动机在于推动网络安全领域的博弈论与人工智能研究，初始应用场景聚焦于机器人学。实验结果表明，EF在探索机器人网络安全机器学习方面具备有效性。基于EF构建的人工智能代理通过强化学习，在性能上超越了暴力破解方法与人类专家方案，为后续利用ExploitFlow开展研究奠定了基础。然而，我们亦识别出EF驱动代理的若干局限性，包括过拟合倾向、泛化所需数据集的稀缺性与生产成本问题，以及跨不同安全设置时网络状态解析的挑战。为在发挥ExploitFlow优势的同时解决上述不足，我们提出以ExploitFlow为核心的综合性自动化渗透测试框架构想——Malism。