Cyber-Physical AI & Robotics Security
When AI operates physical machinery—autonomous vehicles, drones, industrial cobots—software exploits trigger real-world hazards. Security auditing requires evaluating sensor spoofing, actuator control loops, and hardware safety interlocks.
See the workflow →The Four Physical Audit Domains
Auditing embodied AI bridges neural perception networks with mechanical control systems.
- Sensor spoofing & injection
- Control loop & CAN bus hijacking
- Sim-to-Real policy gaps
- Hardware fail-safe interlocks
Core Concepts
Understanding the convergence of neural decision-making and physical safety engineering.
Perception & Sensor Spoofing
Embodied AI relies on physical sensors (LIDAR, RADAR, cameras, MEMS gyroscopes). Attackers inject physical signals—laser pulses, ultrasonic tones, optical patterns—to blind or deceive the AI perception pipeline, forcing incorrect spatial trajectory decisions.
- LIDAR & RADAR spoofing: injecting phantom obstacle reflections
- Acoustic attacks: tricking MEMS gyroscopes in autonomous drones
- GPS spoofing & jamming: hijacking spatial navigation modules
- Physical adversarial stickers: deceiving road camera perception
Actuator Control & Hardware Interlocks
AI policies issue commands to actuators via vehicle buses (CAN, Automotive Ethernet, ROS2). Auditing verifies that isolated hardware safety interlocks (ISO 26262, ISO 10218) override malfunctioning or compromised AI neural controllers instantly.
- CAN bus injection: bypassing neural control to command actuators directly
- ROS2 node security: auditing inter-process communication between AI modules
- Latency & loop exhaustion: delaying control signals to induce instability
- Hardware interlock testing: verifying physical emergency stop override
The Audit Workflow
A Hardware-in-the-Loop (HIL) testing methodology for physical AI machinery.
Analyze cyber-physical boundary
Map neural perception models, sensor interfaces, microcontrollers, and physical actuator control buses.
Execute sensor signal injection
Apply physical and optical perturbations (laser spoofing, acoustic interference, physical patches) to perception inputs.
Test Hardware-in-the-Loop (HIL) control
Simulate neural policy failures and adversarial inputs while measuring physical actuator responses and latency limits.
Verify physical fail-safe isolation
Ensure hardware-level safety interlocks independently halt machinery when neural policies violate safety envelopes.
Related Pages
Deep dives into reinforcement learning policies, vision models, and penetration testing.
Adversarial Attacks on RL
Attacking reinforcement learning policies at inference time in physical environments.
Multimodal & Vision Security
Physical adversarial patches, image perturbations, and camera perception security.
Networked Systems & AI Audit
Securing distributed agent networks, wireless signals, and robotics communication.
AI Governance & EU AI Act
High-risk AI compliance requirements for safety-critical machinery and vehicles.