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Neural networks are well-known to be vulnerable to imperceptibleperturbations in the input, called adversarial examples, that result inmisclassification. Generating adversarial examples for source code poses anadditional challenge compared to the domains of images and natural language,because source code perturbations must retain the functional meaning of thecode. We identify a striking relationship between token frequency statisticsand learned token embeddings: the L2 norm of learned token embeddings increaseswith the frequency of the token except for the highest-frequnecy tokens. Weleverage this relationship to construct a simple and efficient gradient-freemethod for generating state-of-the-art adversarial examples on models of code.Our method empirically outperforms competing gradient-based methods with lessinformation and less computational effort.

STRATA: Simple, Gradient-Free Attacks for Models of Code