The rapid advancement of Artificial Intelligence (AI) has enabled deep learning models to operate not only in the cloud but also directly on edge devices and microcontrollers. This shift addresses critical needs such as privacy preservation, reduced latency, and energy efficiency, but it also introduces new security challenges. Edge-deployed models are inherently exposed to attacks that aim to extract knowledge, reduce accuracy, or compromise system reliability. This thesis investigates the problem of AI security on edge devices, focusing on resource-constrained microcontrollers. After reviewing the context and major threats, one of the most critical attacks for embedded systems is implemented and analyzed: Model Extraction via Output Matching.