Physical AI Security
Physical AI extends AI into embodied and real-time settings where sensing, communication, timing, control, and actuation all become part of the security story.
Overview
Physical AI covers systems such as robots, vehicles, drones, and autonomous machines that do not only infer but also act. In these systems, security and safety become tightly coupled.
Threat model
Attackers may spoof sensors, exploit latency, tamper with hardware, interfere with communication, or manipulate perception-to-action loops. The consequence is often more than wrong output; it can affect physical behavior.
Countermeasures
Defenses require multi-layer thinking: secure sensing, trusted compute, bounded control logic, resilient communication, runtime anomaly detection, fail-safe behavior, and hardware/software co-design.
Open challenges
The hardest issue is integration. It is still difficult to evaluate physical AI systems under realistic combined threats spanning perception, control, hardware, and real-world operating conditions.