This paper focuses on these evolving cyber-physical threats under the assumption that conventional perimeter defenses may be breached. Such attacks often manipulate measurements, control references, or learned signals at the cyber-physical interface to degrade operation while remaining stealthy. In converter-dominated grids, these can lead to degraded system performance, unnecessary curtailment, equipment stress, financial loss, or, in severe cases, loss of grid stability. Accordingly, this article surveys cyberattacks targeting converter-dominated systems and classifies them by their technical mechanisms and points of entry. The objective is to dissect these attack modalities in the context of modern IoT-based energy systems and to identify where current security frameworks fall short. To this end, this survey places particular emphasis on data-driven approaches that can assess and detect attacks at both the device and system levels, especially when attackers adapt over time or when system knowledge is incomplete. These insights are then synthesized into an integrated attack surface assessment framework that enables systematic vulnerability identification and detection enhancement.