| Title | Discovery of AI/ML Supply Chain Vulnerabilities within Automotive Cyber-Physical Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Williams, Daniel, Clark, Chelece, McGahan, Rachel, Potteiger, Bradley, Cohen, Daniel, Musau, Patrick |
| Conference Name | 2022 IEEE International Conference on Assured Autonomy (ICAA) |
| Keywords | artificial intelligence, Autonomous vehicles, Autonomous Vulnerability Discovery, composability, compositionality, Cyber Dependencies, Cyber-physical systems, Human Behavior, human factors, machine learning, machine learning algorithms, Metrics, pubcrawl, resilience, Resiliency, Safety, Scalability, Software, Software algorithms, software reliability, supply chain, Supply chains |
| Abstract | Steady advancement in Artificial Intelligence (AI) development over recent years has caused AI systems to become more readily adopted across industry and military use-cases globally. As powerful as these algorithms are, there are still gaping questions regarding their security and reliability. Beyond adversarial machine learning, software supply chain vulnerabilities and model backdoor injection exploits are emerging as potential threats to the physical safety of AI reliant CPS such as autonomous vehicles. In this work in progress paper, we introduce the concept of AI supply chain vulnerabilities with a provided proof of concept autonomous exploitation framework. We investigate the viability of algorithm backdoors and software third party library dependencies for applicability into modern AI attack kill chains. We leverage an autonomous vehicle case study for demonstrating the applicability of our offensive methodologies within a realistic AI CPS operating environment. |
| DOI | 10.1109/ICAA52185.2022.00020 |
| Citation Key | williams_discovery_2022 |
Discovery of AI/ML Supply Chain Vulnerabilities within Automotive Cyber-Physical Systems