Max Taylor

Arthur Amorim, Paul Gazzillo, Max Taylor et al.

Standard communication protocols for Unmanned Aerial Vehicles (UAVs), such as MAVLink, lack the capability to enforce the contextual validity of message sequences. Autopilots therefore remain vulnerable to stealthy attacks, where syntactically correct but semantically ill-timed commands induce unsafe states without triggering physical anomaly detectors. Prior work (DATUM) demonstrated that global Refined Multiparty Session Types (RMPSTs) are an effective specification language for centralized MAVLink protocol enforcement, but suffered from two engineering failures: manual proof terms interleaved with protocol definitions, and an OCaml extraction backend whose managed runtime is incompatible with resource-constrained UAV hardware. We present Platum, a framework that addresses both failures with a minimal DSL requiring only the five semantic components of a global session type (sender, receiver, label, payload variable, refinement predicate), whose structural well-formedness conditions are confirmed via reflective decision procedures in Meta-F*. Confirmed specifications are compiled directly into flat, allocation-free C Finite State Machines (FSMs), deployed as centralized proxy monitors at the GCS/UAV communication boundary. Our evaluation demonstrates a 4x reduction in total monitor latency and lower memory overhead compared to DATUM, measured via ArduPilot SITL simulation.

Max Taylor, Haicheng Chen, Feng Qin et al.

Control firmware in unmanned aerial vehicles (UAVs) uses sensors to model and manage flight operations, from takeoff to landing to flying between waypoints. However, sensors can fail at any time during a flight. If control firmware mishandles sensor failures, UAVs can crash, fly away, or suffer other unsafe conditions. In-situ model checking finds sensor failures that could lead to unsafe conditions by systematically failing sensors. However, the type of sensor failure and its timing within a flight affect its manifestation, creating a large search space. We propose Avis, an in-situ model checker to quickly uncover UAV sensor failures that lead to unsafe conditions. Widely used control firmware already support operating modes. Avis injects sensor failures as the control firmware transitions between modes - a key execution point where mishandled software exceptions can trigger unsafe conditions. We implemented Avis and applied it to ArduPilot and PX4. Avis found unsafe conditions 2.4X faster than Bayesian Fault Injection, the leading, state-of-the-art approach. Within the current code base of ArduPilot and PX4, Avis discovered 10 previously unknown software bugs that lead to unsafe conditions. Additionally, we reinserted 5 known bugs that caused serious, unsafe conditions and Avis correctly reported all of them.