A Black-Box Attack on Code Models via Representation Nearest Neighbor Search

Understand

Existing methods for generating adversarial code examples face several challenges: limted availability of substitute variables, high verification costs for these substitutes, and the creation of adversarial samples with noticeable perturbations.

• To address these concerns, our proposed approach, RNNS, uses a search seed based on historical attacks to find potential adversarial substitutes.
• Rather than directly using the discrete substitutes, they are mapped to a continuous vector space using a pre-trained variable name encoder.
• Based on the vector representation, RNNS predicts and selects better substitutes for attacks.