Graph-structured data underpin a wide spectrum of modern applications. However, complex graph topologies and homophilic patterns can facilitate attribute inference attacks (AIAs) by enabling sensitive information leakage to propagate across local neighborhoods. Existing AIAs predominantly assume that adversaries can probe sensitive attributes through repeated model queries. Such assumptions are often impractical in real-world settings due to stringent data protection regulations, prohibitive query budgets, and heightened detection risks, especially when inferring multiple sensitive attributes. More critically, this model-centric perspective obscures a pervasive blind spot: \textbf{intrinsic multiple sensitive information leakage arising solely from publicly released graphs.} To exploit this unexplored vulnerability, we introduce a new attack paradigm and propose \textbf{Taipan, the first query-free transfer-based attack framework for multiple sensitive attribute inference attacks on graphs (G-MSAIAs).} Taipan integrates \emph{Hierarchical Attack Knowledge Routing} to capture intricate inter-attribute correlations, and \emph{Prompt-guided Attack Prototype Refinement} to mitigate negative transfer and performance degradation. We further present a systematic evaluation framework tailored to G-MSAIAs. Extensive experiments on diverse real-world graph datasets demonstrate that Taipan consistently achieves strong attack performance across same-distribution settings and heterogeneous similar- and out-of-distribution settings with mismatched feature dimensionalities, and remains effective even under rigorous differential privacy guarantees. Our findings underscore the urgent need for more robust multi-attribute privacy-preserving graph publishing methods and data-sharing practices.

翻译：图结构数据支撑着众多现代应用。然而，复杂的图拓扑结构和同配性模式会促使敏感信息在局部邻域间传播泄露，从而助长属性推断攻击。现有攻击方法大多假设攻击者能够通过重复的模型查询来探测敏感属性。在实际场景中，由于严格的数据保护法规、有限的查询预算以及较高的检测风险——尤其是在推断多个敏感属性时——此类假设往往难以成立。更为关键的是，这种以模型为中心的视角掩盖了一个普遍存在的盲区：\textbf{仅从公开发布的图中便可能产生固有的多敏感信息泄露}。为利用这一尚未被探索的脆弱性，我们提出了一种新的攻击范式，并设计了\textbf{太攀蛇——首个面向图多敏感属性推断攻击的免查询迁移式攻击框架}。该框架集成了\textit{分层攻击知识路由}以捕捉复杂的属性间关联，并采用\textit{提示引导的攻击原型精炼}来缓解负迁移与性能退化问题。我们进一步提出了一个专为图多敏感属性推断攻击设计的系统性评估框架。在多个真实世界图数据集上的大量实验表明，太攀蛇在相同分布设定下，以及在特征维度不匹配的异构相似分布与分布外设定下，均能持续实现强劲的攻击性能，甚至在严格的差分隐私保护下依然有效。我们的研究结果凸显了开发更鲁棒的多属性隐私保护图发布方法与数据共享实践的迫切性。