Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing trigger generators are often simplistic in structure and overly reliant on specific features, confining them to a single graph learning paradigm, such as graph supervised learning, graph contrastive learning, or graph prompt learning. Such paradigm-specific designs lead to poor transferability across different learning frameworks, limiting attack success rates in general testing scenarios. To bridge this gap, we propose Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers(CP-GBA), which employs Graph Prompt Learning(GPL) to synthesize transferable subgraph triggers. Specifically, we first distill a compact yet expressive trigger set into a queryable repository, jointly optimizing for class-awareness, feature richness, and structural fidelity. Furthermore, we pioneer the theoretical exploration of GPL transferability under prompt-based objectives, ensuring robust generalization to diverse and unseen test-time paradigms. Extensive experiments across multiple real-world datasets and defense scenarios show that CP-GBA achieves state-of-the-art attack success rates.

翻译：图神经网络（GNN）易受后门攻击，攻击者通过植入恶意触发器操纵模型预测。现有触发器生成器结构简单且过度依赖特定特征，仅适用于单一图学习范式（如图监督学习、图对比学习或图提示学习）。这种范式特定的设计导致攻击在不同学习框架间迁移性差，在通用测试场景中攻击成功率受限。为弥合这一差距，我们提出面向可提示子图触发的跨范式图后门攻击（CP-GBA），该方法利用图提示学习（GPL）合成可迁移的子图触发器。具体而言，我们首先将紧凑且富有表达力的触发器集蒸馏为可查询的存储库，联合优化类别感知性、特征丰富性和结构保真度。此外，我们率先在理论上探索了基于提示目标的GPL迁移性，确保对多样化的未知测试范式具有鲁棒泛化能力。在多个真实世界数据集和防御场景中的大量实验表明，CP-GBA实现了最先进的攻击成功率。