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The article explores the vulnerability of social recommender systems to multi-agent adversarial attacks under a black-box scenario. Topics discussed include the application of Graph Neural Networks (GNNs) in social recommendations, the impact of untargeted attacks using fake user profiles and malicious social relations, and the introduction of a multi-agent reinforcement learning framework, MultiAttack, to coordinate these attacks effectively.