[…]In this paper we present and evaluate a new approach to extract feedback for fuzzing on embedded devices using information the power consumption leaks. Side-channel aware fuzzing is a threefold process that is initiated by sending an input to a target device and measuring its power consumption. First, we extract features from the power traces of the target device using machine learning algorithms. Subsequently, we use the features to reconstruct the code structure of the analyzed firmware. In the final step we calculate a score for the input, which is proportional to the code coverage. We carry out our proof of concept by fuzzing synthetic software and a light-weight AES implementation running on an ARM Cortex-M4 microcontroller. Our results show that the power side-channel carries information relevant for fuzzing.