PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

—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 PGF UZZ , 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. PGF UZZ 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 PGF UZZ to increase the efficiencyofthefuzzingprocesssignificantly.Wevalidate PGF UZZ on three RV control programs, ArduPilot, PX4, and Paparazzi, with 56 unique policies. PGF UZZ discovered 156 previously unknown bugs, 106 of which have been acknowledged by their developers.