← Back to all news
arXiv Machine Learning By Minhua Lin, Zhicheng Gao, Yilong Wang, Hanqing Lu, Xiang Zhang, Suhang Wang

Trojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Models

Read the original on arXiv Machine Learning →

The paper introduces STAG, a stealthy trojan attack framework targeting Graph Foundation Models (GFMs) that operate on text‑attributed graphs (TAGs). STAG jointly generates graph triggers and soft‑prompt text cues so that both modalities converge to a malicious target class while keeping the trigger subgraph structurally similar to the original and the trigger text readable. Experiments on several TAG datasets and GFMs confirm that STAG achieves high attack success rates while remaining difficult to detect.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jul 23

FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense

arXiv:2607. 19674v1 Announce Type: cross Abstract: Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches that lack semantic understanding, making them vulnerable to stealthy triggers and harmful to benign structures.

By Chenyu Zhou, Yabin Peng, Wei Huang, Kunlin Li, Shuaishuai Zhang, Xinyuan Miao
llmsfine-tuning
More like this →
arXiv Machine Learning
Sep 4

LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models

The paper introduces PromptGFM, a Graph Foundation Model designed for text‑attributed graphs (TAGs). It integrates Large Language Models (LLMs) and Graph Neural Networks (GNNs) through a Graph Understanding Module that prompts LLMs to emulate GNN workflows, and a Graph Inference Module that creates a language‑based graph vocabulary for better alignment and scalability. Experiments show PromptGFM outperforms existing methods and transfers effectively across various graphs and tasks.

By Xi Zhu, Haochen Xue, Ziwei Zhao, Wujiang Xu, Jingyuan Huang, Minghao Guo, Qifan Wang, Kaixiong Zhou, Imran Razzak, Yongfeng Zhang
llmsfine-tuningsafety
More like this →