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Although deep learning (DL) has recently received significant attention in accelerated MRI, recent studies suggest that small perturbations may lead to large instabilities in DL-based reconstructions. This has also highlighted concerns for their utility in clinical settings. However, these works focus on single-coil acquisitions, which are not practically relevant. In this work, we investigate how small adversarial perturbations affect multi-coil MRI reconstruction, particularly using conventional non-DL methods. Our results indicate that for multi-coil MRI reconstruction, conventional parallel imaging and multi-coil compressed sensing (CS) methods also exhibit considerable instabilities against small adversarial perturbations. Moreover, for physics-guided DL reconstructions that utilize the forward encoding operator explicitly, such small perturbations predominantly target the linear data-consistency units. These results suggest that at high acceleration rates, adversarial attacks exploit the ill-conditioning of the forward encoding operator.