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

arXiv: https://arxiv.org/abs/2401.16448 · HTML: https://mlsanity.com/paper/2401.16448/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model](https://mlsanity.com/paper/2304.15010/) (2023)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [Benchmarking Large Language Models for Automated Verilog RTL Code Generation](https://mlsanity.com/paper/2212.11140/) (2022)
- [nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models](https://mlsanity.com/paper/2303.04864/) (2023)
- [(Security) Assertions by Large Language Models](https://mlsanity.com/paper/2306.14027/) (2023)
- [Fixing Hardware Security Bugs with Large Language Models](https://mlsanity.com/paper/2302.01215/) (2023)
- [The Cost of Application-Class Processing: Energy and Performance Analysis of a Linux-ready 1.7GHz 64bit RISC-V Core in 22nm FDSOI Technology](https://mlsanity.com/paper/1904.05442/) (2019)
- [Delving into Macro Placement with Reinforcement Learning](https://mlsanity.com/paper/2109.02587/) (2021)

## Built on by (most cited first, 18 of 18)

- [A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages](https://mlsanity.com/paper/2410.03981/) (2024)
- [LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust](https://mlsanity.com/paper/2405.07061/) (2024)
- [SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning](https://mlsanity.com/paper/2504.10369/) (2025)
- [From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification](https://mlsanity.com/paper/2504.19959/) (2025)
- [SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation](https://mlsanity.com/paper/2405.16072/) (2024)
- [SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models](https://mlsanity.com/paper/2506.20415/) (2025)
- [MARVEL: Multi-Agent RTL Vulnerability Extraction using Large Language Models](https://mlsanity.com/paper/2505.11963/) (2025)
- [HLSDebugger: Identification and Correction of Logic Bugs in HLS Code with LLM Solutions](https://mlsanity.com/paper/2507.21485/) (2025)
- [FVDebug: An LLM-Driven Debugging Assistant for Automated Root Cause Analysis of Formal Verification Failures](https://mlsanity.com/paper/2510.15906/) (2025)
- [CktEvo: Repository-Level RTL Code Benchmark for Design Evolution](https://mlsanity.com/paper/2603.08718/) (2026)
- [Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Design](https://mlsanity.com/paper/2507.16226/) (2025)
- [VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog](https://mlsanity.com/paper/2604.15375/) (2026)
- [Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPT](https://mlsanity.com/paper/2405.01419/) (2024)
- [HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databases](https://mlsanity.com/paper/2505.15701/) (2025)
- [TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs](https://mlsanity.com/paper/2506.17894/) (2025)
- [Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimization](https://mlsanity.com/paper/2506.00002/) (2025)
- [DUET: Agentic Design Understanding via Experimentation and Testing](https://mlsanity.com/paper/2512.06247/) (2025)
- [SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection](https://mlsanity.com/paper/2603.05689/) (2026)

## Abstract (arXiv)

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.