State-of-the-art drone path planners enable drones to autonomously navigate around obstacles in GPS-denied, uncharted and cluttered environments. However, our investigation shows that path planners fail to maneuver drones correctly in specific scenarios, leading to incidents such as collisions. To minimize such risks, drone path planners should be tested thoroughly against diverse scenarios before deployment. Existing research for testing drones to uncover safety-critical vulnerabilities is only focused on the flight control programs and is limited in the capability to generate diverse obstacle scenarios for testing drone path planners.

In this work, we propose \textit{DPFuzzer}, an automated framework for testing drone path planners. \textit{DPFuzzer} is an evolutionary algorithm (EA) based testing framework. It aims to uncover vulnerabilities in drone path planners by generating diverse critical scenarios that can trigger vulnerabilities. To better guide the critical scenario generation, we introduce \textit{Environmental Risk Factor (ERF)}, a metric we propose, to abstract potential safety threats of scenarios. We evaluate \textit{DPFuzzer} on state-of-the-art drone path planners and the experimental result shows that \textit{DPFuzzer} can effectively find diverse vulnerabilities. Additionally, we demonstrate that these vulnerabilities are exploitable in the real world on commercial drones.