<p>This article formulates interactive adversarial differential graphical games for synchronization control of multiagent systems (MASs) subject to adversarial inputs interacting with the systems through topology communications. Local control and interactive adversarial inputs affect each agent’s local synchronization error via local networks. The distributed global Nash equilibrium (NE) solutions are guaranteed in the games by solving the optimal control input of each agent and the worst-case adversarial input based solely on local states and communications. The asymptotic stability of the local synchronization error dynamics and the NE are guaranteed. Furthermore, the authors devise a data-driven online reinforcement learning (RL) algorithm that only computes the distributed Nash control online using system trajectory data, eliminating the need for explicit system dynamics. A simulation-based example validates the game and algorithm.</p>