# Emerging AI Security Threats for Autonomous Cars -- Case Studies

arXiv: https://arxiv.org/abs/2109.04865 · HTML: https://mlsanity.com/paper/2109.04865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Adversarial Music: Real World Audio Adversary Against Wake-word Detection System](https://mlsanity.com/paper/1911.00126/) (2019)
- [Failure Modes in Machine Learning Systems](https://mlsanity.com/paper/1911.11034/) (2019)

## Built on by (most cited first, 1 of 1)

- [Enhancing TinyML Security: Study of Adversarial Attack Transferability](https://mlsanity.com/paper/2407.11599/) (2024)

## 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.