arXiv Machine Learning June 8, 2026 By Jing Xu, Franziska Boenisch, Adam Dziedzic

ADAGE: Active Defenses Against GNN Extraction

Read the original on arXiv Machine Learning →

arXiv:2503. 00065v4 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems.

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

4d ago

Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks

arXiv:2609.37972v1 Announce Type: cross Abstract: As Graph Neural Networks (GNNs) are widely deployed as Machine Learning-as-a-Service (MLaaS) APIs, model stealing attacks have emerged as a critical...

By Ying Song, Xiaowei Jia, Balaji Palanisamy

benchmarks

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 4

Mitigating Backdoors via Decoy Shortcuts and Knowledge Decoupling

arXiv:2608. 00732v1 Announce Type: new Abstract: Backdoor attacks pose a serious threat to deep neural networks, especially when training relies on third-party data, allowing adversaries to inject malicious behaviors through data poisoning.

By Zixuan Zhu, Rui Wang, Lihua Jing, Jinwen Zhong

benchmarks

arXiv AI

Jun 3

Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models

arXiv:2506. 03087v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial analysis, leading to a growing demand for model transparency.

By Bin Ma, Yuyuan Feng, Minhua Lin, Enyan Dai

safety

arXiv Machine Learning

Aug 6

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP

arXiv:2505. 19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks.

By Binyan Xu, Xilin Dai, Di Tang, Kehuan Zhang

llms multimodal 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

One email a morning, machine-written

Related stories

Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks

Defending against Model Extraction for GNNs with Model Reprogramming

Mitigating Backdoors via Decoy Shortcuts and Knowledge Decoupling

Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP

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