Red Team LLM: towards an adaptive and robust automation solution
Résumé
Artificial intelligence has become really popular in recent years, especially its embedding in cybersecurity applications. Today, studies have shown that reinforcement learning agents are able to find the optimal sequence of actions in order to attack a network. However, these agents are often over-trained and can neither adapt nor be robust to different networks from the ones they were trained on. We propose a new agent based on a zero-shot approach that adapts itself to any given network and that is robust to parameters and objectives changes without requiring another training phase. We introduce a new metric that better measures the ability of agents to attack a network without prior knowledge. In this paper, we also discuss about the first steps towards explainability for our model and its future improvements.
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