A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights From Highly Autonomous Aggressive Malware

Artificial general intelligence (AGI) promises transformative societal changes but poses safety and containment challenges. Large language models such as ChatGPT have intensified public expectations and apprehensions regarding AGI capabilities and risks. Existing research underestimated replicating human intelligence and lacks effective containment strategies scaled for AGI's complexity. We developed a cybersecurity-inspired framework to reconceptualize AGI containment as securing critical infrastructure indispensable for its operation. We applied game theory to model the strategic interplay between AGI and humans, drawing parallels with highly autonomous malware, emphasizing infrastructural dependencies and human countermeasures. We introduced offensive/defensive containment strategies and an AGI Kill Chain model profiling escalating AGI threats. Our game-theoretic approach examined complex AGI-human interactions revealing insights for adaptive oversight mechanisms. Game simulations demonstrated AGI carefully manages resources and autonomy balancing benefits against risks, necessitating strategic human responses. Our findings provided detailed containment tactics, emphasizing flexibility to address AGI's dynamic evolution. We proposed comprehensive, multidisciplinary containment strategies, effective governance evaluating long-term efficacy, and emphasize ongoing innovation for aligning AGI progression with utility and security.