AI Security Threats against Pervasive Robotic Systems

As AI-enabled robots become increasingly integrated into homes, industries, and critical infrastructure, they also become attractive targets for sophisticated cyberattacks. This course provides an in-depth exploration of the security landscape surrounding pervasive robotic systems.

Through a structured 7-module curriculum, participants will study and experiment with a range of attack vectors, including sensor spoofing, data poisoning, adversarial inference, and actuator manipulation. Each module combines theoretical foundations with practical labs using real or simulated robots, AI frameworks, and security testing tools.

Students will learn to identify vulnerabilities, conduct penetration testing, apply defensive strategies (like sensor redundancy, robust AI modeling, and control signal verification), and evaluate ethical and legal implications of securing robotics. By the end of the course, learners will gain the critical knowledge and hands-on experience needed to secure next-generation robotic systems against evolving AI-driven threats.