# SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection

arXiv: https://arxiv.org/abs/2603.05689 · HTML: https://mlsanity.com/paper/2603.05689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)
- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 30 of 31)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [AutoChip: Automating HDL Generation Using LLM Feedback](https://mlsanity.com/paper/2311.04887/) (2023)
- [Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correction](https://mlsanity.com/paper/2406.00115/) (2024)
- [HDLdebugger: Streamlining HDL debugging with Large Language Models](https://mlsanity.com/paper/2403.11671/) (2024)
- [MEIC: Re-thinking RTL Debug Automation using LLMs](https://mlsanity.com/paper/2405.06840/) (2024)
- [Fixing Hardware Security Bugs with Large Language Models](https://mlsanity.com/paper/2302.01215/) (2023)
- [Multitask-based Evaluation of Open-Source LLM on Software Vulnerability](https://mlsanity.com/paper/2404.02056/) (2024)
- [LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debugging](https://mlsanity.com/paper/2401.16448/) (2024)
- [Self-HWDebug: Automation of LLM Self-Instructing for Hardware Security Verification](https://mlsanity.com/paper/2405.12347/) (2024)
- [Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge](https://mlsanity.com/paper/2402.01728/) (2024)
- [MARVEL: Multi-Agent RTL Vulnerability Extraction using Large Language Models](https://mlsanity.com/paper/2505.11963/) (2025)
- [BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection](https://mlsanity.com/paper/2505.22878/) (2025)
- [Towards LLM-based Root Cause Analysis of Hardware Design Failures](https://mlsanity.com/paper/2507.06512/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security verification remains limited due to scarcity of publicly available hardware description language (HDL) datasets. This knowledge gap constrains LLM performance in detecting vulnerabilities within HDL designs. To address this challenge, we propose SecureRAG-RTL, a novel Retrieval-Augmented Generation (RAG)-based approach that significantly enhances LLM-based security verification of hardware designs. Our approach integrates domain-specific retrieval with generative reasoning, enabling models to overcome inherent limitations in hardware security expertise. We establish baseline vulnerability detection rates using prompt-only methods and then demonstrate that SecureRAG-RTL achieves substantial improvements across diverse LLM architectures, regardless of size. On average, our method increases detection accuracy by about 30%, highlighting its effectiveness in bridging domain knowledge gaps. For evaluation, we curated and annotated a benchmark dataset of 14 HDL designs containing real-world security vulnerabilities, which we will release publicly to support future research. These findings underscore the potential of RAG-driven augmentation to enable scalable, efficient, and accurate hardware security verification workflows.