[2601.15474] BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack

Computer Science > Machine Learning arXiv:2601.15474 (cs) [Submitted on 21 Jan 2026 (v1), last revised 8 Apr 2026 (this version, v2)] Title:BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack Authors:Md Nabi Newaz Khan, Abdullah Arafat Miah, Yu Bi View a PDF of the paper titled BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack, by Md Nabi Newaz Khan and 2 other authors View PDF HTML (experimental) Abstract:Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to backdoor attacks. Existing studies on backdoor attack for graph classification are limited to single target attack using subgraph replacement based mechanism where the attacker implants only one trigger into the GNN model. In this paper, we introduce the first multi-targeted backdoor attack for graph classification task, where multiple triggers simultaneously redirect predictions to different target labels. Instead of subgraph replacement, we propose subgraph injection which preserves the structure of the original graphs while poisoning the clean graphs. Extensive experiments demonstrate the efficacy of our approach, where our attack achieves high attack success rates for all target labels with minimal impact on the clean accuracy. Experimental results on five dataset demonstrate the superior performance of our attack framework compared to the conventional subgraph replacement-based attack. Our anal...