  The rise of online social networks has facilitated the evolution of social
recommender systems, which incorporate social relations to enhance users'
decision-making process. With the great success of Graph Neural Networks in
learning node representations, GNN-based social recommendations have been
widely studied to model user-item interactions and user-user social relations
simultaneously. Despite their great successes, recent studies have shown that
these advanced recommender systems are highly vulnerable to adversarial
attacks, in which attackers can inject well-designed fake user profiles to
disrupt recommendation performances. While most existing studies mainly focus
on targeted attacks to promote target items on vanilla recommender systems,
untargeted attacks to degrade the overall prediction performance are less
explored on social recommendations under a black-box scenario. To perform
untargeted attacks on social recommender systems, attackers can construct
malicious social relationships for fake users to enhance the attack
performance. However, the coordination of social relations and item profiles is
challenging for attacking black-box social recommendations. To address this
limitation, we first conduct several preliminary studies to demonstrate the
effectiveness of cross-community connections and cold-start items in degrading
recommendations performance. Specifically, we propose a novel framework
Multiattack based on multi-agent reinforcement learning to coordinate the
generation of cold-start item profiles and cross-community social relations for
conducting untargeted attacks on black-box social recommendations.
Comprehensive experiments on various real-world datasets demonstrate the
effectiveness of our proposed attacking framework under the black-box setting.
