mlsanityData to 7 Oct 2026

Fixing Hardware Security Bugs with Large Language Models

arXiv 2302.01215 ↗First posted 2 Feb 2023cs.CRcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 6; 2025: 8; 2026: 4.

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  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
  2. 2023VeriGen: A Large Language Model for Verilog Code Generationcited by 197
  3. 2023LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automationcited by 40
  4. 2023Fully Autonomous Programming with Large Language Modelscited by 31
  5. 2024LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debuggingcited by 18
  6. 2024LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trustcited by 18
  7. 2024Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledgecited by 10
  8. 2024Compiler generated feedback for Large Language Modelscited by 9
  9. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  10. 2023FLAG: Finding Line Anomalies (in code) with Generative AIcited by 7
  11. 2023ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patchingcited by 6
  12. 2025SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Modelscited by 5
12 more
  1. 2025Towards LLM-based Root Cause Analysis of Hardware Design Failurescited by 4
  2. 2024LLM-aided explanations of EDA synthesis errorscited by 3
  3. 2025PerfCoder: Large Language Models for Interpretable Code Performance Optimizationcited by 3
  4. 2026EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolutioncited by 2
  5. 2024Digital ASIC Design with Ongoing LLMs: Strategies and Prospectscited by 1
  6. 2025HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databasescited by 1
  7. 2025Empirical evaluation of LLMs in predicting fixes of Configuration bugs in Smart Home Systemcited by 0
  8. 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniquescited by 0
  9. 2025Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspectivecited by 0
  10. 2026SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detectioncited by 0
  11. 2026EstRTL: Functional Estimation Guided RTL Code Generationcited by 0
  12. 2026VeriPilot: An LLM-Powered Verilog Debugging Frameworkcited by 0