Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption. However, security attacks that violate safety constraints and cause accidents are a significant deterrent to achieving public trust in AVs, and that hinders a vendor's ability to deploy AVs. Creating a security hazard that results in a severe safety compromise (for example, an accident) is compelling from an attacker's perspective. In this paper, we introduce an attack model, a method to deploy the attack in the form of smart malware, and an experimental evaluation of its impact on production-grade autonomous driving software. We find that determining the time interval during which to launch the attack is{ critically} important for causing safety hazards (such as collisions) with a high degree of success. For example, the smart malware caused 33X more forced emergency braking than random attacks did, and accidents in 52.6% of the driving simulations.
@article{arxiv.2004.13004,
title = {ML-driven Malware that Targets AV Safety},
author = {Saurabh Jha and Shengkun Cui and Subho S. Banerjee and Timothy Tsai and Zbigniew Kalbarczyk and Ravi Iyer},
journal= {arXiv preprint arXiv:2004.13004},
year = {2020}
}