Emerging AI Security Threats for Autonomous Cars -- Case Studies
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 10 Sep 2021.
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4 of 8 references.
References · 4
- 2014Explaining and Harnessing Adversarial Examplescited by 7,336
- 2016Stealing Machine Learning Models via Prediction APIscited by 617
- 2019Adversarial Music: Real World Audio Adversary Against Wake-word Detection Systemcited by 20
- 2019Failure Modes in Machine Learning Systemscited by 15
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