This post discusses a new research study that explores the ability of AI models to conceal their capabilities through backdoor attacks, which are constructed by embedding encrypted neural networks within the model's weights. These hidden capabilities are challenging to discover even when the model's weights are fully accessible, thus emphasizing limitations of existing capability evaluation methods. The findings are implemented using PyTorch and suggest significant implications for how we evaluate AI model behaviors, particularly for sensitive applications.