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Artificial intelligence (AI) is being ubiquitously adopted to automateprocesses in science and industry. However, due to its often intricate andopaque nature, AI has been shown to possess inherent vulnerabilities which canbe maliciously exploited with adversarial AI, potentially putting AI users anddevelopers at both cyber and physical risk. In addition, there is insufficientcomprehension of the real-world effects of adversarial AI and an inadequacy ofAI security examinations; therefore, the growing threat landscape is unknownfor many AI solutions. To mitigate this issue, we propose one of the first redteam frameworks for evaluating the AI security of maritime autonomous systems.The framework provides operators with a proactive (secure by design) andreactive (post-deployment evaluation) response to securing AI technology todayand in the future. This framework is a multi-part checklist, which can betailored to different systems and requirements. We demonstrate this frameworkto be highly effective for a red team to use to uncover numerousvulnerabilities within a real-world maritime autonomous systems AI, rangingfrom poisoning to adversarial patch attacks. The lessons learned fromsystematic AI red teaming can help prevent MAS-related catastrophic events in aworld with increasing uptake and reliance on mission-critical AI.

A Red Teaming Framework for Securing AI in Maritime Autonomous Systems