CodeCipher: Learning To Obfuscate Source Code Against LLMs

Yalan Lin, Chengcheng Wan, Yixiong Fang, Xiaodong Gu

Keywords: Code obfuscation; LLM privacy; Large Language Models of Code

TL;DR: A learning based code obfuscation method to protect code privacy from LLM service providers

Abstract: While code large language models have made significant strides in AI-assisted coding tasks, there are growing concerns about privacy challenges. The user code is transparent to the cloud LLM service provider, inducing risks of unauthorized training, reading, and execution of the user code. In this paper, we propose CodeCipher, a novel method that perturbs privacy from code while preserving the original response from LLMs. CodeCipher transforms the LLM's embedding matrix so that each row corresponds to a different word in the original matrix, forming a token-to-token confusion mapping for obfuscating source code. The new embedding matrix is optimized through minimizing the task-specific loss function. To tackle the challenge from the discrete and sparse nature of word vector spaces, CodeCipher adopts a discrete optimization strategy that aligns the updated vector to the nearest valid token in the vocabulary before each gradient update. We demonstrate the effectiveness of our approach on three AI-assisted coding tasks including code completion, summarization, and translation. Results show that our model successfully confuses the privacy in source code while preserving the original LLM's performance.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 3384