Hyungsub Kim (Purdue University), Muslum Ozgur Ozmen (Purdue University), Antonio Bianchi (Purdue University), Z. Berkay Celik (Purdue University), Dongyan Xu (Purdue University)

Robotic vehicles (RVs) are becoming essential tools of modern systems, including autonomous delivery services, public transportation, and environment monitoring. Despite their diverse deployment, safety and security issues with RVs limit their wide adoption. Most attempts to date in RV security aim to propose defenses that harden their control program against syntactic bugs, input validation bugs, and external sensor spoofing attacks. In this paper, we introduce PGFUZZ, a policy-guided fuzzing framework, which validates whether an RV adheres to identified safety and functional policies that cover user commands, configuration parameters, and physical states. PGFUZZ expresses desired policies through temporal logic formulas with time constraints as a guide to fuzz the analyzed system. Specifically, it generates fuzzing inputs that minimize a distance metric measuring ``how close'' the RV current state is to a policy violation. In addition, it uses static and dynamic analysis to focus the fuzzing effort only on those commands, parameters, and environmental factors that influence the ``truth value'' of any of the exercised policies. The combination of these two techniques allows PGFUZZ to increase the efficiency of the fuzzing process significantly. We validate PGFUZZ on three RV control programs, ArduPilot, PX4, and Paparazzi, with 56 unique policies. PGFUZZ discovered 156 previously unknown bugs, 106 of which have been acknowledged by their developers.

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Model-Agnostic Defense for Lane Detection against Adversarial Attack

Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

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Screen Gleaning: Receiving and Interpreting Pixels by Eavesdropping on...

Zhuoran Liu, Léo Weissbart, Dirk Lauret (Radboud University)

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