Generating Adversarial Computer Programs using Optimized Obfuscations

Understand

Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed.

• These models have demonstrated success in applications such as auto-completing code, summarizing large programs, and detecting bugs and malware in programs.
• In this work, we investigate principled ways to adversarially perturb a computer program to fool such learned models, and thus determine their adversarial robustness.
• We use program obfuscations, which have conventionally been used to avoid attempts at reverse engineering programs, as adversarial perturbations.