While Graph Foundation Models (GFMs) have achieved notable progress across diverse tasks recently, their robustness under domain noise, structural perturbations, and adversarial attacks remains largely underexplored. A core limitation lies in inadequate modeling of hierarchical structural semantics, which are intrinsic priors and critical for generalization. In this work, we propose SA^2GFM, a robust GFM framework that enhances domain adaptable representations through Structure Aware Semantic Augmentation. First, to embed 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 robust and transferable representations through structure guided compression. To mitigate 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 optimizes hierarchical structures through joint intra and inter community structure learning. Extensive experiments validate the superiority of SA^2GFM in effectiveness and robustness against random noise and adversarial perturbations on node and graph classification, compared with nine state of the art baselines.

Large vision-language models (VLMs) have made significant progress in the field of response generation. However, these models also face more potential security risks due to diverse media inputs. In realistic settings, adversaries often only have access to black-box models, which makes query-based attacks on black-box models an important attack method. Although this method has high accuracy, it also suffers from the issue of low attack efficiency. To address this challenge, we propose an optimization method based on prior information. This method uses the gradient generated in the surrogate white-box model as prior information to guide the query-based attack in the black-box model, reducing the number of unnecessary queries and thus improving the attack efficiency. We conduct comprehensive experiments using various models to demonstrate the availability and universality of the optimization method based on prior gradient information.

Graph Foundation Models hold significant potential for advancing multi-domain graph learning, yet their full capabilities remain largely untapped. Existing works show promising task performance with the “pretrain-then-prompt” paradigm, which lacks theoretical foundations to understand why it works and how much knowledge can be transferred from source domains to the target. In this paper, we introduce BRIDGE, a bounded graph foundation model pre-trained on multi-domains with Generalization guarantees. To learn discriminative source knowledge, we align multi-domain graph features with domain-invariant aligners during pre-training. Then, a lightweight Mixture of Experts (MoE) network is proposed to facilitate downstream prompting through self-supervised selective knowledge assembly and transfer. Further, to determine the maximum amount of transferable knowledge, we derive an optimizable generalization error upper bound from a graph spectral perspective given the Lipschitz continuity. Extensive experiments demonstrate the superiority of BRIDGE on both node and graph classification compared with 15 state-of-the-art baselines.

Inspired by the remarkable success of foundation models in language and vision, Graph Foundation Models (GFMs) hold significant promise for broad applicability across diverse graph tasks and domains. However, existing GFMs struggle with unstable few-shot fine-tuning, where both performance and adaptation efficiency exhibit significant fluctuations caused by the randomness in the support sample selection and structural discrepancies between the pre-trained and target graphs. How to fine-tune GFMs robustly and efficiently to enable trustworthy knowledge transfer across domains and tasks is the major challenge. In this paper, we propose **GRAVER**, a novel **G**enerative g**RA**ph **V**ocabulari**E**s for **R**obust GFM fine-tuning framework that tackles the aforementioned instability via generative augmentations. Specifically, to identify transferable units, we analyze and extract key class-specific subgraph patterns by ego-graph disentanglement and validate their transferability both theoretically and empirically. To enable effective pre-training across diverse domains, we leverage a universal task template based on ego-graph similarity and construct graph vocabularies via graphon-based generative experts. To facilitate robust and efficient prompt fine-tuning, we grave the support samples with in-context vocabularies, where the lightweight MoE-CoE network attentively routes knowledge from source domains. Extensive experiments demonstrate the superiority of GRAVER over **effectiveness**, **robustness**, and **efficiency** on downstream few-shot node and graph classification tasks compared with **15** state-of-the-art baselines.