collaborators

10 papers

cs.CR2026

Your "Pro" LLM Subscription May Actually Be "Free": Exposing Fingerprint Spoofing Risks in LLM Inference Services

Jiahao Zhang, Xiuyu Li, Suhang Wang

As Large Language Model (LLM) APIs become ubiquitous, users increasingly rely on black-box fingerprinting to verify that providers are serving the advertised premium models. Howeve…

cs.LG2026

The Confidence Trap: Calibration Attacks for Graph Neural Networks

Cuong Dang, Jiahao Zhang, Hieu Ta Quang +3

While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to adversarial structural perturbations…

cs.IR2026

Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search

Jiahao Zhang, Shaofei Huang, Yaxiong Wang +1

Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotation. To mitigate this, existing…

cs.AI2026

Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems

Shuhua Yang, Jiahao Zhang, Yilong Wang +2

Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning. While prior work shows that Graph…

cs.LG2026

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks

Jiahao Zhang, Yilong Wang, Suhang Wang

Graph neural networks (GNNs) are widely used for learning from graph-structured data in domains such as social networks, recommender systems, and financial platforms. To comply wit…

cs.CL2026

URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models

Vinh Nguyen, Cuong Dang, Jiahao Zhang +6

Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. However, current RAG evaluati…