Natural attack for pre-trained models of code
Zhou Yang, Jieke Shi, Junda He, David Lo · Proceedings of the 44th International Conference on Software Engineering · 2022
Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly perturb model inputs to make a victim model produce wrong outputs. Current works mainly attack models of code with examples that preserve operational program semantics but ignore a fundamental requirement for adversarial example generation: perturbations should be natural to human judges, which we refer to as naturalness requirement.