  Graph Neural Networks (GNNs) are powerful in learning rich network
representations that aid the performance of downstream tasks. However, recent
studies showed that GNNs are vulnerable to adversarial attacks involving node
injection and network perturbation. Among these, node injection attacks are
more practical as they don't require manipulation in the existing network and
can be performed more realistically. In this paper, we propose a novel problem
statement - a class-specific poison attack on graphs in which the attacker aims
to misclassify specific nodes in the target class into a different class using
node injection. Additionally, nodes are injected in such a way that they
camouflage as benign nodes. We propose NICKI, a novel attacking strategy that
utilizes an optimization-based approach to sabotage the performance of
GNN-based node classifiers. NICKI works in two phases - it first learns the
node representation and then generates the features and edges of the injected
nodes. Extensive experiments and ablation studies on four benchmark networks
show that NICKI is consistently better than four baseline attacking strategies
for misclassifying nodes in the target class. We also show that the injected
nodes are properly camouflaged as benign, thus making the poisoned graph
indistinguishable from its clean version w.r.t various topological properties.
