Obfuscation stands as a promising solution for safeguarding hardware intellectual property (IP) against a spectrum of threats including reverse engineering, IP piracy, and tampering. In this paper, we introduce Obfus-chat, a novel framework leveraging Generative Pre-trained Transformer (GPT) models to automate the obfuscation process. The proposed framework accepts hardware design netlists and key sizes as inputs, and autonomously generates obfuscated code tailored to enhance security. To evaluate the effectiveness of our approach, we employ the Trust-Hub Obfuscation Benchmark for comparative analysis. We employed SAT attacks to assess the security of the design, along with functional verification procedures to ensure that the obfuscated design remains consistent with the original. Our results demonstrate the efficacy and efficiency of the proposed framework in fortifying hardware IP against potential threats, thus providing a valuable contribution to the field of hardware security.

翻译：混淆技术作为保护硬件知识产权（IP）免受逆向工程、IP盗用和篡改等多种威胁的有效解决方案。本文提出Obfus-chat——一种创新框架，利用生成式预训练Transformer（GPT）模型自动化混淆流程。该框架以硬件设计网表和密钥长度作为输入，自主生成针对安全性优化的混淆代码。为评估方法有效性，我们采用Trust-Hub混淆基准进行对比分析。通过SAT攻击评估设计安全性，同时执行功能验证流程确保混淆设计保持与原设计一致。实验结果表明，该框架在加固硬件IP抵御潜在威胁方面具有显著效能，为硬件安全领域做出了重要贡献。