While Graph Foundation Models (GFMs) have achieved notable progress across diverse tasks recently, their robustness under domain noise, structural perturbations, and even adversarial attacks remains largely underexplored. A core limitation lies in the inadequate modeling of hierarchical structural semantics, which are intrinsic priors and critical for generalization. In this work, we propose SA²GFM, a robust GFM framework that enhances the domain-adaptable representations through Structure-Aware Semantic Augmentation. First, to embed the hierarchical structural priors, we transform entropy-based encoding trees into structure-aware textual prompts for feature augmentation. The enriched inputs are processed by a novel self-supervised Information Bottleneck mechanism that distills the robust and transferable representations through structure-guided compression. To mitigate the negative transfer in cross-domain adaptation, we develop an expert adaptive routing mechanism that integrates a mixture-of-experts architecture with a null expert design. To enable efficient downstream adaptation, we propose a fine-tuning module that efficiently optimizes the hierarchical structures through the joint intra- and inter-community structure learning. Extensive experiments validate the superiority of SA²GFM over effectiveness and robustness against random noise and adversarial perturbations on node and graph classification compared with 9 state-of-the-art baselines.