GoldenFuzz: Generative Golden Reference Hardware Fuzzing
Lichao Wu, Mohamadreza Rostami, Huimin Li, Nikhilesh Singh, Ahmad-Reza Sadeghi

TL;DR
GoldenFuzz is a two-stage hardware fuzzing framework that uses a fast, ISA-compliant Golden Reference Model to efficiently discover vulnerabilities and improve coverage in complex hardware systems, outperforming existing fuzzers.
Contribution
It introduces a novel two-stage fuzzing approach with a Golden Reference Model, enabling rapid test case refinement and enhanced vulnerability discovery in hardware systems.
Findings
Outperforms existing fuzzers in coverage and efficiency
Uncovers all known vulnerabilities and discovers five new ones
Identifies two previously unknown vulnerabilities in commercial hardware
Abstract
Modern hardware systems, driven by demands for high performance and application-specific functionality, have grown increasingly complex, introducing large surfaces for bugs and security-critical vulnerabilities. Fuzzing has emerged as a scalable solution for discovering such flaws. Yet, existing hardware fuzzers suffer from limited semantic awareness, inefficient test refinement, and high computational overhead due to reliance on slow device simulation. In this paper, we present GoldenFuzz, a novel two-stage hardware fuzzing framework that partially decouples test case refinement from coverage and vulnerability exploration. GoldenFuzz leverages a fast, ISA-compliant Golden Reference Model (GRM) as a ``digital twin'' of the Device Under Test (DUT). It fuzzes the GRM first, enabling rapid, low-cost test case refinement, accelerating deep architectural exploration and vulnerability…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Security and Verification in Computing · Radiation Effects in Electronics
