ML-driven Malware that Targets AV Safety

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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.


Saurabh Jha (University of Illinois at Urbana-Champaign)
Shengkun Cui (University of Illinois at Urbana-Champaign)
Subho S. Banerjee (University of Illinois at Urbana-Champaign)
James Cyriac (University of Illinois at Urbana-Champaign)
Zbigniew T. Kalbarczyk (University of Illinois at Urbana-Champaign)
Ravishankar K. Iyer (University of Illinois at Urbana-Champaign)

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