Large Language Models for Code Obfuscation Evaluation of the Obfuscation Capabilities of OpenAI's GPT-3.5 on C Source Code

This study explores the efficacy of large language models, specifically GPT-3.5, in obfuscating C source code for software protection. We utilized eight distinct obfuscation techniques in tandem with seven representative C code samples to conduct a comprehensive analysis. The evaluation was performed using a Python-based tool we developed, which interfaces with the OpenAI API to access GPT-3.5. Our metrics of evaluation included the correctness and diversity of the obfuscated code, along with the robustness of the resultant protection. While the diversity of the resulting code was found to be commendable, our findings indicate a prevalent issue with the correctness of the obfuscated code and the overall level of protection provided. Consequently, we assert that while promising, the feasibility of deploying large language models for automatic code obfuscation is not yet sufficiently established. This study signifies an important step towards understanding the limitations and potential of AI-based code obfuscation, thereby informing future research in this area.