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