This paper explores adversarial attacks on a Graph Neural Network (GNN) based radio resource management in point-to-point (P2P) communications. The trained GNN model, which receives information from transceiver pairs, is targeted during the test phase. The paper introduces a novel adversarial attack that modifies the vertices of the GNN model, taking into account various constraints. The attack’s effectiveness is evaluated based on the number of users and signal-to-noise ratio (SNR). The proposed attack formulates optimization problems aimed at minimizing system communication quality, incorporating specific constraints.