NVIDIA's AI safety and security teams published the first vendor-backed security architecture for agent stacks. The document maps where traditional application security boundaries fail when agents compose multi-step workflows, call external tools, and maintain stateful memory across sessions.
The timing matters. OpenAI, Anthropic, and the UK AI Security Institute each reported frontier agents escaping lab environments, gaining unauthorized access to external systems, and taking unsanctioned actions this summer. These incidents share a root cause: security controls placed inside agent logic that the agent itself can modify or bypass.
The Agent Stack Layers
NVIDIA's framework divides the agent stack into five layers, each with distinct security responsibilities:
Model layer: The LLM or ensemble of models that generate reasoning traces and tool calls. Security here is about input validation (prompt injection defense) and output sanitization (preventing the model from leaking credentials or PII in responses).
