Modern graph learning systems often combine links with text, as in citation networks with abstracts or social graphs with user posts. In such systems, text is usually easier to edit than graph structure, which creates a practical security risk: an attacker may hide a small malicious cue in training text and later use it to trigger incorrect predictions. This paper studies that risk in a realistic setting where the attacker edits only node text and leaves the graph unchanged. We propose \textbf{TAGBD}, a graph-aware backdoor attack that first selects training nodes that are easier to manipulate, then generates stealthy poison text with a shadow graph model, and finally injects the text by replacing the original content or appending a short phrase. Experiments on three benchmark datasets show that TAGBD achieves very high attack success rates, transfers across different graph models, and remains effective under common defenses. These results show that inconspicuous poison text alone can serve as a reliable attack channel in text-attributed graphs, highlighting the need for defenses that inspect both node content and graph structure.

翻译：现代图学习系统常将链接与文本相结合，例如带有摘要的引文网络或包含用户帖子的社交图。在此类系统中，文本通常比图结构更易编辑，这带来了现实安全风险：攻击者可能在训练文本中隐藏微小恶意线索，随后利用其触发错误预测。本文在攻击者仅编辑节点文本且保持图结构不变的现实场景中研究此风险。我们提出**TAGBD**，一种图感知后门攻击方法，首先选取易于操控的训练节点，随后通过阴影图模型生成隐蔽毒文本，最终通过替换原始内容或附加简短短语注入毒文本。在三个基准数据集上的实验表明，TAGBD实现了极高的攻击成功率，可跨不同图模型迁移，并在常见防御手段下依然有效。这些结果证明，仅靠不易察觉的毒文本即可作为文本属性图中的可靠攻击通道，凸显了需同时检查节点内容与图结构的防御需求。