2 citations · 6 across the 37 of their papers we have counts for
16 papers · 1 filter
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs
Jinbo Liu, Defu Cao, Yifei Wei +6
Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a…
A Systematic Study of Model Extraction Attacks on Graph Foundation Models
Haoyan Xu, Ruizhi Qian, Jiate Li +9
Graph machine learning has advanced rapidly in tasks such as link prediction, anomaly detection, and node classification. As models scale up, pretrained graph models have become va…
Revisiting Multivariate Time Series Forecasting with Missing Values
Jie Yang, Yifan Hu, Kexin Zhang +3
Missing values are common in real-world time series, and multivariate time series forecasting with missing values (MTSF-M) has become a crucial area of research for ensuring reliab…
Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses
Lincan Li, Bolin Shen, Chenxi Zhao +4
Graph-structured data, which captures non-Euclidean relationships and interactions between entities, is growing in scale and complexity. As a result, training state-of-the-art grap…
A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Kaixiang Zhao, Lincan Li, Kaize Ding +3
Machine learning (ML) models have significantly grown in complexity and utility, driving advances across multiple domains. However, substantial computational resources and speciali…
DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
Kaixiang Zhao, Joseph Yousry Attalla, Qian Lou +1
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such…
A Survey on Model Extraction Attacks and Defenses for Large Language Models
Kaixiang Zhao, Lincan Li, Kaize Ding +3
Model extraction attacks pose significant security threats to deployed language models, potentially compromising intellectual property and user privacy. This survey provides a comp…
Learning from the Storm: A Multivariate Machine Learning Approach to Predicting Hurricane-Induced Economic Losses
Bolin Shen, Eren Erman Ozguven, Yue Zhao +3
Florida is particularly vulnerable to hurricanes, which frequently cause substantial economic losses. While prior studies have explored specific contributors to hurricane-induced d…
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
Zebin Wang, Menghan Lin, Bolin Shen +4
Graph Neural Networks (GNNs) have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service (MLaaS) a viable…
Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making
Xiaopeng Yuan, Xingjian Zhang, Ke Xu +5
Large language models (LLMs) are increasingly used for tasks that require complex reasoning. Most benchmarks focus on final outcomes but overlook the intermediate reasoning steps -…
MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
Xueqi Cheng, Minxing Zheng, Shixiang Zhu +1
Model extraction attacks aim to replicate the functionality of a black-box model through query access, threatening the intellectual property (IP) of machine-learning-as-a-service (…
TyphoFormer: Language-Augmented Transformer for Accurate Typhoon Track Forecasting
Lincan Li, Eren Erman Ozguven, Yue Zhao +3
Accurate typhoon track forecasting is crucial for early system warning and disaster response. While Transformer-based models have demonstrated strong performance in modeling the te…
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
Haoyan Xu, Zhengtao Yao, Xuzhi Zhang +6
Out-of-distribution (OOD) detection is critical for ensuring the safety and reliability of machine learning systems, particularly in dynamic and open-world environments. In the vis…
Few-Shot Graph Out-of-Distribution Detection with LLMs
Haoyan Xu, Zhengtao Yao, Yushun Dong +4
Existing methods for graph out-of-distribution (OOD) detection typically depend on training graph neural network (GNN) classifiers using a substantial amount of labeled in-distribu…
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
Zhan Cheng, Bolin Shen, Tianming Sha +3
Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attack…
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments
Kaixiang Zhao, Lincan Li, Kaize Ding +3
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data. With the increas…