Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (3): 1-22.doi: 10.11896/jsjkx.250700093

• Intelligent Information System Based on AGI Technology • Previous Articles     Next Articles

Survey of Backdoor Attacks and Defenses on Graph Neural Network

DING Yan1, DING Hongfa1,2, YU Muran1, JIANG Heling1   

  1. 1 School of Information, Guizhou University of Finance and Economics, Guiyang 550025, China
    2 Guizhou Province Key Laboratory of Sovereign Blockchain, Guizhou University of Finance and Economics, Guiyang 550025, China
  • Received:2025-07-15 Revised:2025-10-28 Published:2026-03-12
  • About author:DING Yan,born in 2001,postgraduate,is a member of CCF(No.Y5913G).Her main research interests include data security,graph backdoor and detection.
    DING Hongfa,born in 1988,Ph.D,associate professor,master’s supervisor,is a member of CCF(No.36866M).His main research interests include data security and privacy protection,cryptographic algorithm and protocol design.
  • Supported by:
    National Natural Science Foundation of China(62566006) and Student Research Project of Guizhou University of Finance and Economics(2025BAZYSY038).

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Abstract: In artificial intelligence(AI)-driven intelligent information systems,GNNs are extensively utilized for knowledge discovery and decision support in critical domains including social network analysis and financial risk control,leveraging their superior graph-structured data modeling capabilities.However,the heavy reliance of such systems on third-party data and models exposes GNNs to stealthy backdoor attacks.Attackers can inject backdoor triggers or tamper with models to induce predetermined erroneous outputs for inputs containing specific patterns,thereby undermining the trustworthiness and reliability of intelligent information services.To ensure the security and controllability of intelligent information systems,this paper systematically reviews research on GNN backdoor attacks and defenses through dual data-model perspectives.It firstly conducts in-depth analysis of attack vectors during data collection,model training,and deployment phases,establishing a comprehensive attack-defense framework.It subsequently categorizes attacks into six data-oriented and two model-oriented types based on implementation stages and attacker capabilities,classifies defenses into data-oriented,model-oriented,and robust training-oriented approaches according to deployment stages and defender capacities,with detailed comparative examination of their core mechanisms,technical features,advantages,and limitations.Finally,it summarizes current research challenges while outlining future directions.The proposed attack-defense taxonomy facilitates profound understanding of GNN backdoor threat evolution and advances security design for next-generation trustworthy intelligent information systems.

Key words: Graph neural network, Backdoor attack, Backdoor defense, Backdoor trigger, Data privacy and security, Intelligent information systems

CLC Number: 

  • TP309.2
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