Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis, based on their powerful learning capabilities on graph data. Nevertheless, the predictive performance of GNNs relies on the quality of task-specific node labels, so it is a common practice to improve the model's generalization ability in the downstream execution of decision-making tasks through pre-training. Graph prompting is a prudent choice but would be risky without taking measures to prevent data leakage. In other words, in high-risk decision scenarios, prompt learning can infer private information by accessing model parameters trained on private data (publishing model parameters in pre-training, i.e., without directly leaking the raw data, is a tacitly accepted trend). However, myriad graph inference attacks necessitate tailored module design and processing to enhance inference capabilities due to variations in the optimization direction of supervision signals. In this paper, we propose a novel Prompt-based unifying Inference Attack framework on GNNs, named ProIA. Specifically, ProIA retains the crucial topological information of the graph during pre-training, enhancing the background knowledge of the inference attack model. It then utilizes a unified prompt and introduces additional disentanglement factors in downstream attacks to adapt to task-relevant knowledge.
Finally, extensive experiments show that ProIA enhances attack capabilities and demonstrates exceptional adaptability to various inference attacks.