Robotic aerial vehicles (RAVs), particularly drones, are crucial in civil and military sectors. However, researchers have found that adversaries can inject noise into sensor measurements and cause physical impacts on the RAVs like crashes. Although identifying such signal injection attacks is essential to evaluate and improve the robustness of an RAV, it is challenging to discover them since their impact depends on the RAV’s physical states and the search space of noise signals and physical states is vast due to its dynamic nature.This paper proposes IMUFUZZER, a feedback-driven fuzzing framework, to automatically test an RAVs system and discover signal injection attacks. IMUFUZZER generates realistic noise signals for various inertial measurement unit (IMU) sensors, and monitors their impact on RAV control to detect mission failures, leveraging a high-fidelity RAV simulator. To find the physical states that attacks depend on, IMUFUZZER generates various mission paths that the RAV will fly through. We develop a novel feedback mechanism to quantify the resilience of the RAV against attacks and efficiently guide the fuzzing process to find signal injection attacks. Using IMUFUZZER, we have discovered 23 successful signal injection attacks on popular RAV control software (ArduPilot). We evaluate the correctness and effectiveness of our feedback-based sensor fuzzing and demonstrate the feasibility of the discovered attacks through physical experiments.

Robotic vehicles are playing an increasingly important role in our daily life. Unfortunately, attackers have demonstrated various sensor spoofing attacks that interfere with robotic vehicle operations, imposing serious threats. Thus, it is crucial to discover such attacks earlier than attackers so that developers can secure the vehicles. In this paper, we propose a new sensor fuzzing framework SensorFuzz that can systematically discover potential sensor spoofing attacks on robotic vehicles. It generates malicious sensor inputs by formally modeling the existing sensor attacks and leveraging high-fidelity vehicle simulation, and then analyzes the impact of the inputs on the vehicle with a resilience-based feedback mechanism.