  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.
