LLM4SecHW: Leveraging Domain-Specific Large Language Model for Hardware Debugging

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.

I. Introduction

Designing a modern hardware is becoming increasingly challenging due to the complexity of chips for applications such as IoT, AI, and Quantum Computing [1] . These intricate hardware designs are hard to test and verify, raising the risk of hidden bugs and vulnerabilities. One major reason is that existing verification and testing approaches often require the manual creation of assertions, data models, and test vectors [2] . Furthermore, some vulnerabilities may not affect all functionalities of design, making sole reliance on functional verification insufficient for ensuring system robustness and reliability [3] , [4] . Considering that flaws in hardware design can be primary sources of potential security vulnerabilities, it is essential to automatically identify and fix hardware bugs with minimal human intervention during the design phase. Fine-tuning Large Language Model (LLM)s for domain-specific tasks has seen successes in fields like medicine [5] and software design [6] . LLMs have been at the forefront of advancements in numerous software programming-related tasks, demonstrating their potential in automating tasks like auto code completion, malware detection, and code refactoring [7] .