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In guidance published September 21, 2026, NVIDIA argues that securing AI agents requires enforceable controls across the systems they use, not just instructions telling a model how to behave. Its engineering advice focuses on limiting what an agent can access, recording its actions and testing whether those protections hold.
Keep Permissions Outside the Agent
NVIDIA divides an agent system into models, harnesses that organize tools and workflows, and the runtime where actions execute. The runtime should restrict files, network destinations and processes independently of the agent's decisions. Each agent should also have a traceable identity and credentials limited to its assigned task.
For developers, that means permission to perform one task must not silently authorize a different one. NVIDIA says consequential actions and changes to access rights should require human approval; an agent may request more access but should not grant it to itself.
Test the Boundaries
Protected records of tool calls, authorization decisions and outcomes can help security teams reconstruct an incident. NVIDIA also recommends checking the source and integrity of agent tools and dependencies, with procedures ready to revoke access or contain a failure.
Before deployment, teams should test whether controls block attempts to reach out-of-scope credentials, send sensitive data to unauthorized destinations, change permissions or interfere with monitoring. NVIDIA calls for repeating those tests after material changes to models, tools or workflows, and for assigning an owner to act on failures.
Tools Are Examples, Not Proof
NVIDIA points to its open-source OpenShell runtime as one way to enforce policies outside an agent's control, alongside integrations and security tools from other companies. The post does not provide test results showing that these products meet its proposed standard. Its practical measure of security is whether teams can reproduce failures, verify fixes and keep those checks working across releases.
Source: AI Security Is an Engineering Problem — How to Solve It at Every Layer of the Agent Stack
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