Published in ICML 2026, 2026
This paper proposes DQA learning that learn black-box attack against LLMR without rely on specific victim query.
Recommended citation: Jiate Li, Defu Cao, Li Li, Wei Yang, Yuehan Qin, Chenxiao Yu, Tiannuo Yang, Ryan A Rossi, Yan Liu, Xiyang Hu, Yue Zhao. (2026). "“Someone Hid It”: Query-Agnostic Black-Box Attacks on LLM-Based Retrieval." ICML 2026.
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Published in CVPR 2025, 2025
This paper extends AGNNCert to defending posioning attack.
Recommended citation: Jiate Li, Meng Pang, Yun Dong, Binghui Wang. (2025). "Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations." CVPR 2025.
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Published in ICLR 2025, 2025
This paper proposed a provably robust framework for GNN explainers.
Recommended citation: Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang. (2025). "Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks." ICLR 2025.
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Published in Usenix Security 2025, 2025
This paper proposed an effective certifiable robust GNN method against arbitary perturbations
Recommended citation: Jiate Li, Binghui Wang. (2025). "AGNNCert: Defending Graph Neural Networks against Arbitrary Perturbations with Deterministic Certification." Usenix Security 2025.
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Published in AAAI 2025, 2024
This paper proposed a practical and effective black-box attack against LPDG.
Recommended citation: Jiate Li, Meng Pang, Binghui Wang. (2025). "Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs--A Graph Sequential Embedding Method." AAAI 2025.
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Published in ICML 2024, 2024
This paper studies the vulnerability of GNN explanations.
Recommended citation: Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang. (2024). "Graph Neural Network Explanations are Fragile." ICML 2024.
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