  This paper presents LLM4SecHW, a novel framework for hardware debugging that
leverages domain specific Large Language Model (LLM). Despite the success of
LLMs in automating various software development tasks, their application in the
hardware security domain has been limited due to the constraints of commercial
LLMs and the scarcity of domain specific data. To address these challenges, we
propose a unique approach to compile a dataset of open source hardware design
defects and their remediation steps, utilizing version control data. This
dataset provides a substantial foundation for training machine learning models
for hardware. LLM4SecHW employs fine tuning of medium sized LLMs based on this
dataset, enabling the identification and rectification of bugs in hardware
designs. This pioneering approach offers a reference workflow for the
application of fine tuning domain specific LLMs in other research areas. We
evaluate the performance of our proposed system on various open source hardware
designs, demonstrating its efficacy in accurately identifying and correcting
defects. Our work brings a new perspective on automating the quality control
process in hardware design.
