NVIDIA Cosmos — agentic threat model

Utilizes generative models and physical AI. Threats include adversarial examples designed to trick physical perception models, model stealing of proprietary NVIDIA or customer-trained weights, and mis-aligned outputs that could cause physical simulation failures.

Handles massive 3D assets, CAD files, and simulation data. Threats include data poisoning of simulation environments (leading to faulty autonomous vehicle training) and data exfiltration of highly sensitive industrial designs.

Orchestrates complex simulations and robotics control. Threats include tool misuse where the agent interacts unsafely with connected 3D applications, rendering engines, or physical hardware controllers.

Relies on high-performance GPU infrastructure and collaborative servers (like Omniverse Nucleus). Threats include container/host compromise, privilege escalation on GPU clusters, and unauthorized access to exposed collaboration services.

Not certain from the listing — likely relies on standard Omniverse telemetry and simulation validation tools, but specific guardrails, drift detection, or safety-critical monitoring for generative physical AI are not detailed.

Not certain from the listing — while targeting highly regulated industries like automotive and manufacturing implies strict compliance, specific certifications (e.g., ISO 26262 for functional safety or SOC2) are not explicitly detailed in this directory entry.

Designed for real-time collaboration among multiple users and automated agents. Threats include rogue or compromised collaborative agents injecting malicious assets, and A2A trust abuse within shared virtual simulation spaces.