Researchers have developed a method to teach AI models to use the minimum amount of authority required to complete a task. This is done by auditing each action before execution and after observing its effects on six dimensions of risk. The model was trained using this framework on over 1,500 tasks and achieved a safe success rate of 98.48% compared to 64.36% for the base policy. The method aims to reduce excess-authority error events in tool-using agents, but does not replace traditional permission gating systems or sandboxing. --- Why it matters: This research is important because it addresses a critical issue in AI development: ensuring that models use only the authority granted by users and necessary for task completion. Excess-authority errors can lead to security vulnerabilities and compromised user trust. Source: https://arxiv.org/abs/2608.18351