Systems and methods for identifying and remediating security threats against graph neural network models

Abstract

The disclosed computer-implemented method for identifying and remediating security threats against graph neural network models may include (i) analyzing an input format for model data utilized by a graph neural network (GNN) model on a target computing system, (ii) generating probing data corresponding to the input format, (iii) querying the GNN model utilizing the probing data, (iv) building, based on a query response output of the GNN model utilizing the probing data, one or more shadow GNN models, (v) verifying a performance metric of the shadow GNN models against a target performance metric associated with the GNN model, and (vi) performing a security action that protects against a potential security threat against the GNN model when the performance metric of the shadow GNN models is similar to target performance metric associated with the GNN model. Various other methods, systems, and computer-readable media are also disclosed.