Absence seizures in children.

Objective: To evaluate the cybersecurity of automated insulin delivery (AID) systems, a platform was developed, integrating continuous glucose monitors, fluidic systems, insulin controllers, and wireless communication pathways with virtual subjects. Impact Statement: This study provides evidence that AID systems that are vulnerable to cyberattacks pose substantial risk to patient safety. This underscores the need for improved regulation and design of personal, wearable medical devices, through cyberphysiological system modeling. Introduction: AID systems depend on continuous glucose monitors (CGMs) as their primary sensing input, making evaluation of the accuracy, robustness, and security of these sensors essential for safe diabetes management. However, there are limited assessment tools that preserve the communication and timing paths of CGM sensors within an AID system. Methods: We present a modular hardware-in-the-loop platform that reproduces physiological glucose dynamics, bridging the gap between simulation and clinical validation. The system integrates programmable peristaltic pumps, 3D-printed flow cells, glucose sensors, and a real-time virtual subject simulator. Results: The platform generates reproducible glucose trajectories and supports closed-loop operation through integration with an open-source insulin controller. A false data injection attack scenario on the glucose sensor led to an increase in insulin dosage delivered by the control algorithm relative to baseline. If undetected, this could result in a life-threatening condition. Conclusion: The platform enables system-level cybersecurity assessment of AID under dynamic operating conditions and highlights safety-security interdependencies in closed-loop medical therapeutics.