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Physical AI Security

Physical AI security is where AI stops being only a model and becomes part of a physical control loop.

The scope here is embodied systems that perceive the world through sensors, reason over space and tasks, plan actions, and execute those actions through robots, vehicles, cameras, smart spaces, or edge devices.

The security question is not only whether a model can be fooled. It is whether the whole sensor-to-action pipeline can fail safely when perception, planning, software, hardware, humans, and the surrounding environment interact in real time.

Research Frame​

This section treats Physical AI security as a cyber-physical problem with several connected layers:

  • perception and sensor trust
  • vision-language-action models and embodied reasoning
  • world models, planning, control, and runtime constraints
  • simulation, synthetic data, and sim-to-real validation
  • robotics software, edge compute, firmware, and update paths
  • human-robot interaction, workspace safety, and failure containment

What Goes Here​

The material here will collect notes that connect AI behavior with physical consequences:

  • threat models for embodied agents and autonomous machines
  • attack surfaces across sensors, prompts, policies, planners, and actuators
  • safety cases, guardrails, monitors, and fallback mechanisms
  • robotics and cyber-physical system foundations
  • incident studies and failure-mode writeups
  • practical evaluation methods for risky real-world behavior

Working Questions​

  • What changes when an AI system can move, grasp, drive, open doors, or trigger machinery?
  • Which failures come from the model, and which come from the surrounding system?
  • How should simulation evidence be trusted before real-world deployment?
  • Where do classic security controls meet AI safety controls?
  • What does a useful audit checklist look like for Physical AI systems?

The structure is still being refined. For now, the goal is to keep notes close to the actual system boundary: sensors in, decisions in the middle, actions out.