Nvidia Redefines the Benchmark for AI Agent Security

According to AI TIMES, Nvidia said on September 29 that AI agent security should be treated not as a model safety issue but as an engineering problem, and that enforceable controls must be built across every layer from the model to the runtime environment. It said security in particular must operate at boundaries separated from the agent's own judgment, and that critical tasks require a verification framework that includes human approval. The underlying concern is that because agents use external tools and change their behavior depending on the situation, simply leaving existing application security in place is not enough. For that reason, it emphasized designing identity, permissions, networks, logs, and testing as a single package, and proving that actual blocking and logging are taking place. As examples of how to implement this, it cited a secure runtime that enforces policies outside the execution environment and a method for checking whether controls work through attack simulation. Ultimately, the message is that making agents smarter should not matter more than ensuring boundaries remain intact even when they make mistakes or become unstable, and that this must be a prerequisite for deployment.