  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.
