Attribution-guided Adversarial Code Prompt Generation for Code Completion Models

Abstract

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Large language models have made significant progress in code completion, which may further remodel future software development. However, these code completion models are found to be highly risky as they may introduce vulnerabilities unintentionally or be induced by a special input, i.e., adversarial code prompt. Prior studies mainly focus on the robustness of these models, but their security has not been fully analyzed.