Enhancing System Robustness through Adversarial Reinforcement Learning and Large Language Model Reasoning for Automated Vulnerability Assessment in Complex Decision Environments
DOI:
https://doi.org/10.66280/ijair.v1i2.149Abstract
The rapid expansion of socio-technical infrastructures and large-scale autonomous systems has introduced unprecedented levels of complexity, creating emergent vulnerabilities that traditional security frameworks are ill-equipped to manage. As decision environments become increasingly dynamic, the necessity for automated, proactive vulnerability assessment becomes paramount. This research investigates an integrated architectural paradigm that leverages adversarial reinforcement learning and the cognitive reasoning capabilities of large language models to enhance the robustness of complex systems. By synthesizing the competitive optimization of adversarial frameworks with the semantic depth and contextual awareness of generative reasoning agents, we propose a methodology that identifies non-obvious failure modes in high-stakes environments. The study explores the structural trade-offs between computational efficiency and the depth of reasoning, examining how these hybrid systems navigate the tension between rapid response and long-term strategic foresight. Furthermore, the paper addresses the governance and policy implications of deploying such autonomous auditors within critical infrastructure. Through a comprehensive conceptual analysis, we demonstrate that this dual-engine approach—combining the tactical precision of reinforcement learning with the interpretive power of large-scale reasoning—offers a sustainable pathway toward self-healing, resilient architectures. The findings suggest that while technical integration poses significant challenges, the socio-technical benefits of reduced human oversight and increased systemic transparency provide a compelling case for the adoption of automated vulnerability assessment protocols in modern engineering landscapes.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
