arXiv Machine Learning June 9, 2026 By Zhanke Zhou, Bo Han, Xuan Li, Jiangchao Yao, Sanmi Koyejo, Michael K. Ng

Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense

Read the original on arXiv Machine Learning →

arXiv:2606. 08067v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adjacency, e.

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.

arXiv AI

Jun 30

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models

arXiv:2606. 29748v1 Announce Type: new Abstract: The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sensitive.

By Adebayo Keji, Sayanton Dibbo

rag benchmarks safety

arXiv Machine Learning

Aug 13

Defending against Model Extraction for GNNs with Model Reprogramming

arXiv:2608. 11495v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS).

By Yan Wen, Zhenyi Wang, Heng Huang

safety

arXiv Machine Learning

Aug 26

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models

arXiv:2311.16139v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such a...

By Zeyu Song, Ehsanul Kabir, Shagufta Mehnaz

safety

arXiv Machine Learning

Jun 30

Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks

arXiv:2606. 29240v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in modeling complex graph-structured data with multiple node and relation types.

By Honglin Gao, Junhao Ren, Lan Zhao, Yue Yang, Jindong Chang, Gaoxi Xiao

benchmarks safety

arXiv AI

Sep 1

Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL

arXiv:2608.29054v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly i...

By Shuomin Xue, Jingyuan Li, Ju Jia, Jingxuan Yu, Xiaojun Jia

multimodal benchmarks safety

arXiv Machine Learning

Aug 27

Are LLM-Enhanced GNNs Privacy-Safe?

The paper evaluates privacy risks in graph neural networks enhanced by large language models (LLMs). Using a five‑stage framework, the authors test six real‑world text‑attributed graph datasets with 42 model configurations and six privacy attack methods across link, label, and membership inference threats. Results show that LLM‑enhanced GNNs are more vulnerable than shallow baselines, with semantic enrichment amplifying exploitable signals, and that differential privacy can reduce risk but at a significant cost to utility.

By Longzhu He, Zelang Wen, Chaozhuo Li, Sen Su

llms rag safety

One email a morning, machine-written

Related stories

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models

Defending against Model Extraction for GNNs with Model Reprogramming

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models

Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks

Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL

Are LLM-Enhanced GNNs Privacy-Safe?