Vulnerable Code Search: Transferable Attack for Code Language Models

2026-08-26

AI Summary

本文提出一种针对代码语言模型的跨语言对抗攻击方法，通过修改代码标识符而不改变功能，来降低代码搜索工具的检索准确性。

Abstract

Reliable code retrieval is crucial for developer productivity and effective code reuse. However, current neural code language models (CLMs) powering search tools are susceptible to adversarial attacks targeting non-functional textual elements. In this paper, we introduce a programming language-agnostic, transferable, adversarial attack that exploits this CLM vulnerability. Our approach perturbs identifiers within a code snippet without altering the snippet's functionality to artificially align the code with a target query. We demonstrate that our attack, even when computed using smaller code embedding models, such as CodeT5+, is highly effective and transferable to larger, closed-source embedding models, like Voyage-code-3, or LLMs like Gemini-3.1-Pro. Our attack can increase the similarity between the query and arbitrary, irrelevant code snippets, consequently degrading key retrieval metrics such as the Mean Reciprocal Rank (MRR) of state-of-the-art models by up to 77%. The experimental results highlight the fragility of current code search methods and underscore the need for more robust, semantic-aware approaches.

Problem

• adversarial attacks
• code language models
• reliable code retrieval
• vulnerability
• transferable attack

Innovation

• adversarial attack
• code language models
• transferability
• identifier perturbation
• retrieval metrics