TheHuzz: Instruction Fuzzing of Processors Using Golden-Reference Models for Finding Software-Exploitable Vulnerabilities
Aakash Tyagi (1), Addison Crump (1), Ahmad-Reza Sadeghi (2), Garrett, Persyn (1), Jeyavijayan Rajendran (1), Patrick Jauernig (2), and Rahul Kande, (1) ((1) Texas A&M University, College Station, USA, (2) Technische, Universit\"at Darmstadt, Germany)

TL;DR
TheHuzz is a novel hardware fuzzer that analyzes HDL behaviors, generates assembly instructions to increase coverage, and effectively finds hardware bugs and vulnerabilities in processors, outperforming existing methods.
Contribution
TheHuzz introduces a new hardware fuzzing approach that overcomes HDL limitations, models intrinsic hardware behaviors, and improves bug detection efficiency.
Findings
Detected 11 hardware bugs, including 8 new vulnerabilities.
Achieved 1.98x and 3.33x speed improvements over existing fuzzers.
Demonstrated exploits for identified vulnerabilities.
Abstract
The increasing complexity of modern processors poses many challenges to existing hardware verification tools and methodologies for detecting security-critical bugs. Recent attacks on processors have shown the fatal consequences of uncovering and exploiting hardware vulnerabilities. Fuzzing has emerged as a promising technique for detecting software vulnerabilities. Recently, a few hardware fuzzing techniques have been proposed. However, they suffer from several limitations, including non-applicability to commonly used Hardware Description Languages (HDLs) like Verilog and VHDL, the need for significant human intervention, and inability to capture many intrinsic hardware behaviors, such as signal transitions and floating wires. In this paper, we present the design and implementation of a novel hardware fuzzer, TheHuzz, that overcomes the aforementioned limitations and significantly…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Malware Detection Techniques · Physical Unclonable Functions (PUFs) and Hardware Security
