A technical paper titled “Fuzzing Hardware Like Software” was published by researchers at University of Michigan, Google and Virginia Tech. The paper was presented at the 2022 Usenix Security Symposium.
Abstract:
“Hardware flaws are permanent and potent: hardware cannot be patched once fabricated, and any flaws may undermine even formally verified software executing on top. Consequently, verification time dominates implementation time. The gold standard in hardware Design Verification (DV) is dynamic random testing, due to its scalability to large designs. However, given its undirected nature, this technique is inefficient.
Instead of making incremental improvements to existing dynamic hardware verification approaches, we leverage the observation that existing software fuzzers already provide such a solution, and hence adapt them for hardware verification. Specifically, we translate RTL hardware to a software model and fuzz that model directly. The central challenge we address is how to mitigate the differences between the hardware and software execution models. This includes: 1) how to represent test cases, 2) what is the hardware equivalent of a crash, 3) what is an appropriate coverage metric, and 4) how to create a general-purpose fuzzing harness for hardware.
To evaluate our approach, we design, implement, and open-source a Hardware Fuzzing Pipeline that enables fuzzing hardware at scale, using only open-source tools. Using our pipeline, we fuzz five IP blocks from Google’s OpenTitan Root-of-Trust chip, four SiFive TileLink peripherals, three RISC-V CPUs, and an FFT accelerator. Our experiments reveal a two orders-of-magnitude reduction in run time to achieve similar Finite State Machine coverage over traditional dynamic verification schemes, and 26.70% better HDL line coverage than prior work. Moreover, with our bus-centric harness, we achieve over 83% HDL line coverage in four of the five OpenTitan IPs we study—without any initial seeds—and are able to detect all bugs (four synthetic from Hack@DAC and one real) implanted across all five OpenTitan IPs we study, with less than 10 hours of fuzzing.”
Find the technical paper and related video here.
Authors: Timothy Trippel and Kang G. Shin, University of Michigan; Alex Chernyakhovsky, Garret Kelly, and Dominic Rizzo, Google, LLC; Matthew Hicks, Virginia Tech.
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