Cybersecurity in Autonomous AI Robotics: A Review of Emerging Threats, Adversarial Attacks, and Mitigation Techniques

Abstract

Intelligent robotic systems that utilize artificial intelligence (AI), and have been expanding into high-risk applications (e.g., health care, manufacturing/industrial automation, transportation/smart mobility, etc.), require effective cybersecurity measures to maintain both safe operation and dependability. Compared with typical cyber-physical systems, advanced robotic systems include multiple layers (sensing, control, communications, middleware, and/or AI-based decision support) which create a complex and highly connected attack vector. Due to this increased complexity, these types of systems are vulnerable to a wide range of cyber-security threats including; network breaches/intrusions, manipulated sensors/command inputs, firmware backdoor vulnerabilities, adversarial machine-learning attacks, large language model (LLM) exploits/misuse, vulnerabilities in middle ware solutions, and supply chain-based compromises. Each type of threat has the potential to cause unsafe physical actions by the robot, loss of privacy for individuals involved in the use of the robot or related services, loss of availability/service failure for the robot/system/equipment, and cascaded failures within the entire robotic ecosystem. While existing defensive measures (secure communication protocols, runtime monitoring/perception hardening of robots, protection provided by robot operating system protections/middleware security framework) demonstrate positive results in reducing these risks, there is still much work needed particularly at the areas of adaptive defensive capabilities/system-wide security semantics and standardized evaluation metrics for assessing cyber-resilience in AI-enabled robotic systems. This paper provides an all-encompassing taxonomy of threats to robotic cybersecurity/attack vectors and evaluates and analyzes both attack surfaces and defense mechanisms. Additionally, this paper will provide recommendations for addressing identified knowledge gaps and possible paths forward for developing cyber-resilient AI-enabled robotic systems.