Emerging AI Security Threats for Autonomous Cars -- Case Studies
This highlights a critical security problem for autonomous vehicle manufacturers and users, focusing on model theft and its potential impacts, but it is incremental as it builds on existing concerns without introducing new solutions.
The paper addresses model extraction attacks on AI systems in autonomous vehicles, detailing two use-cases and a generic kill-chain that can compromise these cars, emphasizing the need for strategies to mitigate such security risks.
Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning. However, AI models require a large amount of sensitive training data and are usually computationally intensive to build. The commercial value of such models motivates attackers to mount various attacks. Adversaries can launch model extraction attacks for monetization purposes or step-ping-stone towards other attacks like model evasion. In specific cases, it even results in destroying brand reputation, differentiation, and value proposition. In addition, IP laws and AI-related legalities are still evolving and are not uniform across countries. We discuss model extraction attacks in detail with two use-cases and a generic kill-chain that can compromise autonomous cars. It is essential to investigate strategies to manage and mitigate the risk of model theft.