# Fixing Hardware Security Bugs with Large Language Models

arXiv: https://arxiv.org/abs/2302.01215 · HTML: https://mlsanity.com/paper/2302.01215/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2023.

## Built on by (most cited first, 24 of 24)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2401.12224/) (2023)
- [Fully Autonomous Programming with Large Language Models](https://mlsanity.com/paper/2304.10423/) (2023)
- [LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust](https://mlsanity.com/paper/2405.07061/) (2024)
- [LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debugging](https://mlsanity.com/paper/2401.16448/) (2024)
- [Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge](https://mlsanity.com/paper/2402.01728/) (2024)
- [Compiler generated feedback for Large Language Models](https://mlsanity.com/paper/2403.14714/) (2024)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [FLAG: Finding Line Anomalies (in code) with Generative AI](https://mlsanity.com/paper/2306.12643/) (2023)
- [ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patching](https://mlsanity.com/paper/2308.13062/) (2023)
- [SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models](https://mlsanity.com/paper/2506.20415/) (2025)
- [Towards LLM-based Root Cause Analysis of Hardware Design Failures](https://mlsanity.com/paper/2507.06512/) (2025)
- [LLM-aided explanations of EDA synthesis errors](https://mlsanity.com/paper/2404.07235/) (2024)
- [PerfCoder: Large Language Models for Interpretable Code Performance Optimization](https://mlsanity.com/paper/2512.14018/) (2025)
- [EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolution](https://mlsanity.com/paper/2605.30105/) (2026)
- [Digital ASIC Design with Ongoing LLMs: Strategies and Prospects](https://mlsanity.com/paper/2405.02329/) (2024)
- [HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databases](https://mlsanity.com/paper/2505.15701/) (2025)
- [VeriPilot: An LLM-Powered Verilog Debugging Framework](https://mlsanity.com/paper/2606.23759/) (2026)
- [Empirical evaluation of LLMs in predicting fixes of Configuration bugs in Smart Home System](https://mlsanity.com/paper/2502.10953/) (2025)
- [Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques](https://mlsanity.com/paper/2507.13629/) (2025)
- [Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective](https://mlsanity.com/paper/2512.18261/) (2025)
- [SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection](https://mlsanity.com/paper/2603.05689/) (2026)
- [EstRTL: Functional Estimation Guided RTL Code Generation](https://mlsanity.com/paper/2606.09867/) (2026)

## Abstract (arXiv)

Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI's Codex have demonstrated capabilities in many coding-adjacent domains. In this work we consider how LLMs maybe leveraged to automatically repair security relevant bugs present in hardware designs. We focus on bug repair in code written in the Hardware Description Language Verilog. For this study we build a corpus of domain-representative hardware security bugs. We then design and implement a framework to quantitatively evaluate the performance of any LLM tasked with fixing the specified bugs. The framework supports design space exploration of prompts (i.e., prompt engineering) and identifying the best parameters for the LLM. We show that an ensemble of LLMs can repair all ten of our benchmarks. This ensemble outperforms the state-of-the-art Cirfix hardware bug repair tool on its own suite of bugs. These results show that LLMs can repair hardware security bugs and the framework is an important step towards the ultimate goal of an automated end-to-end bug repair framework.