SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection
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 exp...
| Authors | Touseef Hasan, Blessing Airehenbuwa, Nitin Pundir et al. |
|---|---|
| Primary category | cs.CR |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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