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This paper addresses the prevalent lack of tools to facilitate and empowerGame Theory and Artificial Intelligence (AI) research in cybersecurity. Theprimary contribution is the introduction of ExploitFlow (EF), an AI and GameTheory-driven modular library designed for cyber security exploitation. EF aimsto automate attacks, combining exploits from various sources, and capturingsystem states post-action to reason about them and understand potential attacktrees. The motivation behind EF is to bolster Game Theory and AI research incybersecurity, with robotics as the initial focus. Results indicate that EF iseffective for exploring machine learning in robot cybersecurity. An artificialagent powered by EF, using Reinforcement Learning, outperformed bothbrute-force and human expert approaches, laying the path for using ExploitFlowfor further research. Nonetheless, we identified several limitations inEF-driven agents, including a propensity to overfit, the scarcity andproduction cost of datasets for generalization, and challenges in interpretingnetworking states across varied security settings. To leverage the strengths ofExploitFlow while addressing identified shortcomings, we present Malism, ourvision for a comprehensive automated penetration testing framework withExploitFlow at its core.

ExploitFlow, cyber security exploitation routes for Game Theory and AI research in robotics