Enhancing Cyber Attack Autonomy Through Multi-Agent Reinforcement Learning

Advances in artificial intelligence and machine learning are transforming offensive cybersecurity, enabling the creation of autonomous intelligent cyber agents (AICAs) for offensive operations that overcome the limitations of traditional, static red-teaming playbooks. This work leverages deep multi-agent reinforcement learning (MARL) to implement autonomous cyber agents capable of coordinating sophisticated distributed attacks in dynamic environments. Using the network attack simulation (NASim) environment as a testbed, this paper highlights the limitations of single-agent approaches and demonstrates MARL’s potential to enable decentralized, collaborative strategies. Multi-agent systems were implemented for collaborative red team exercises, such as lateral movement and capture the flag. The results indicate that MARL-based agents successfully learn coordinated attack patterns in small-scale environments, which improves stealth and adaptation. However, while MARL demonstrates potential for decentralized collaboration, our evaluation reveals significant scalability challenges as network complexity increases. These findings underscore the importance of AICA in advancing offensive capabilities while identifying the stabilization of multi-agent training in large-scale topologies as a primary bottleneck for future research.