  Graph Neural Networks (GNNs) have significantly advanced various downstream
graph-relevant tasks, encompassing recommender systems, molecular structure
prediction, social media analysis, etc. Despite the boosts of GNN, recent
research has empirically demonstrated its potential vulnerability to backdoor
attacks, wherein adversaries employ triggers to poison input samples, inducing
GNN to adversary-premeditated malicious outputs. This is typically due to the
controlled training process, or the deployment of untrusted models, such as
delegating model training to third-party service, leveraging external training
sets, and employing pre-trained models from online sources. Although there's an
ongoing increase in research on GNN backdoors, comprehensive investigation into
this field is lacking. To bridge this gap, we propose the first survey
dedicated to GNN backdoors. We begin by outlining the fundamental definition of
GNN, followed by the detailed summarization and categorization of current GNN
backdoor attacks and defenses based on their technical characteristics and
application scenarios. Subsequently, the analysis of the applicability and use
cases of GNN backdoors is undertaken. Finally, the exploration of potential
research directions of GNN backdoors is presented. This survey aims to explore
the principles of graph backdoors, provide insights to defenders, and promote
future security research.
