Mission (Im)Possible: Towards Autonomous Discovery of Autopilot Logic Flaws in Open-Source Drone Frameworks
Researchers’ LLM multi-agent system found 105 previously unknown logic flaws in PX4 and ArduPilot autopilots.
Researchers present Impossible Mission Finder (IMF), a multi-agent system that combines static analysis, dynamic execution, and log analysis to discover logic flaws in open-source drone autopilots. An auditor agent proposes hypotheses from program slices, tester agents write and run cases, and a log analyzer checks results against each hypothesis. Evaluated on PX4 and ArduPilot, IMF reported 105 previously unknown logic flaws that can interrupt missions, trigger invalid actions, or allow flight into restricted zones.
- IMF combines auditor, tester, and log-analyzer LLM agents.
- It pairs program slicing with dynamic tests and log validation.
- Testing PX4 and ArduPilot found 105 previously unknown logic flaws.
- Flaws can interrupt missions, cause invalid actions, or enter restricted zones.
From the original article
1 min read at arxiv.orgAutonomous drones are increasingly deployed in safety-critical tasks, yet their autopilot software can still contain subtle logic flaws that lead to unsafe behaviors. These flaws are difficult to discover with existing techniques because they often depend on semantic relationships across modules, commands, and runtime state, and rarely manifest as crashes or detectable failures.
By Ziyi Liang · arxiv.org
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