Revealing the Risk of Hyper-Parameter Leakage in Deep Reinforcement Learning Models

Researchers discovered that hyper-parameters (settings that control how a deep reinforcement learning model learns and behaves) can be leaked from closed-box DRL models, meaning attackers can figure out these secret settings just by observing how the model responds to different situations. They created an attack called HyperInfer that successfully inferred hyper-parameters with over 90% accuracy, showing that even restricted AI models may expose information that was meant to stay hidden.