Security Challenges in AI-Enabled Robotics: Threats and Countermeasures

Abstract

Artificial intelligence (AI) techniques like machine learning and computer vision have enabled transformative capabilities in robotics. However, integrating AI also introduces new vulnerabilities that could be exploited by adversaries to attack robots. This paper reviews emerging security threats and potential countermeasures for AI enabled robotics. Unique threats arise because robot behaviour depends heavily on training data which could be manipulated, and complex learned models whose logic is difficult to interpret. Key threats span the system lifecycle including data poisoning attacks, adversarial examples to fool perception, malware infecting software, supply chain tampering, and loss of control leading to safety risks. To address these concerns, robust training data governance, algorithmic hardening, cybersecurity best practices, safety engineering, and standards will be critical. Specific countermeasures include adversarial training to make models more robust, anomaly detection to catch unusual inputs, hardware roots of trust to verify integrity, fail-safes for safe operation, and regulation of ethical development practices. However, securing AI robotics remains challenging due to the evolving dynamics between attacks and defences, lack of verification methods for learned models, and fragmentation across vendor ecosystems. Safety, ethics, and security must be ingrained throughout the robotics development lifecycle. Collaboration between cybersecurity, AI safety, and engineering communities will be essential to develop holistic solutions. As robots proliferate into physical environments, advances in transparent and verifiable AI will help build trust. This paper provides a comprehensive survey of threats and countermeasures to guide future research towards securing our emerging AI enabled society.