👉 Opponent computing refers to the practice of using artificial intelligence (AI) models, specifically large language models (LLMs), to simulate or counteract the capabilities of an adversary in cybersecurity, particularly in the context of adversarial machine learning. In this scenario, one party (the defender) trains an AI model to detect and defend against attacks or exploits designed by another party (the opponent). This involves generating adversarial examples—inputs that are intentionally crafted to mislead the AI model—and then training the model to recognize and resist these manipulations. The goal is to enhance the robustness of AI systems against potential threats, ensuring they can accurately identify and respond to malicious activities even when faced with sophisticated adversarial tactics. This dynamic interaction between the defender and opponent helps improve the overall security posture of AI-driven systems by continuously adapting to evolving attack strategies.
