Unmanned Aerial Vehicles autonomously perform tasks using state-of-the-art control algorithms. These control algorithms rely on the freshness and correctness of sensor readings.  Incorrect control leads to catastrophic process destabilization.  In this work, we propose ConfuSense aiming to stealthily impact process control via sensor reconfiguration. In the first part, the attacker will inject messages on buses that connect to the sensor. The injected message reconfigures the sensors. The reconfiguration primitives are selectively used to affect the controller (e.g., stall the control computations), unnoticeable to the data consumer. As a consequence, the manipulated sensor values lead to unwanted control actions (e.g., a drone crash). We experimentally demonstrate ConfuSense and investigate its system level effects and consequences. Our findings show that i) reconfiguring sensors can have surprising effects on reported sensor values, and ii) the attacker can stall the overall Kalman Filter state estimation, leading to a complete stop of control computations. This leads to stealthily crashing or deviating over 30 meters the UAV from its trajectory. Our work shows that attacks on sensors are not limited to continuously inducing random measurements, and demonstrate that sensor reconfiguration can completely stall the drone controller. In our experiments, state-of-the-art UAV controller software and countermeasures are unable to handle such manipulations. Hence, we discuss new countermeasures.

• A. Erba, J.H. Castellanos, S. Sihag, S. Zonouz & N.O. Tippenhauer (2025): ConfuSense: Sensor Reconfiguration Attacks for Stealthy UAV Manipulation. – 3rd USENIX Symposium on Vehicle Security and Privacy (VehicleSec 25): 45–62.