Hao Lyu

A Software Bill of Materials (SBOM) is a machine-readable artifact that systematically organizes software information, enhancing supply chain transparency and security. To facilitate the exchange and utilization of SBOMs, organizations such as the Linux Foundation and OWASP have proposed SBOM standards. Following standards, organizations have developed tools for generating and utilizing SBOMs. However, limited research has examined the adherence of these SBOM tools to standard specifications, a gap that could lead to compliance failures and disruptions in SBOM utilization. This paper presents the first large-scale, two-stage empirical analysis of the adherence gap, using our automated evaluation framework, SAP . The evaluation, comprising a baseline evaluation and a one-year longitudinal follow-up, covers 55,444 SBOMs generated by six SBOM tools from 3,287 real-world repositories. Our analysis reveals persistent, fundamental limitations in current SBOM tools: (1) inadequate compliance support with policy requirements; (2) poor tool consistencies, including inter-tool consistency rates as low as 7.84% to 12.77% for package detection across languages, and significant longitudinal inconsistency, where tools show low consistency with their own prior versions; and (3) mediocre to poor accuracy for detailed software information, e.g. , accuracy of package licenses below 20%. We analyze the root causes of these gaps and provide practical solutions. All the code, replication docker image, evaluation results are open sourced at GitHub and Zenodo ¹ for further researches.

The Instruction Set Architecture (ISA) defines processor operations and serves as the interface between hardware and software. As an open ISA, RISC-V lowers the barriers to processor design and encourages widespread adoption, but also exposes processors to security risks such as functional bugs. Processor fuzzing is a powerful technique for automatically detecting these bugs. However, existing fuzzing methods suffer from two main limitations. First, their emphasis on redundant test case generation causes them to overlook cross-processor corner cases. Second, they rely too heavily on coverage guidance. Current coverage metrics are biased and inefficient, and become ineffective once coverage growth plateaus. To overcome these limitations, we propose SimFuzz, a fuzzing framework that constructs a high-quality seed corpus from historical bug-triggering inputs and employs similarity-guided, block-level mutation to efficiently explore the processor input space. By introducing instruction similarity, SimFuzz expands the input space around seeds while preserving control-flow structure, enabling deeper exploration without relying on coverage feedback. We evaluate SimFuzz on three widely used open-source RISC-V processors: Rocket, BOOM, and XiangShan, and discover 17 bugs in total, including 14 previously unknown issues, 7 of which have been assigned CVE identifiers. These bugs affect the decode and memory units, cause instruction and data errors, and can lead to kernel instability or system crashes. Experimental results show that SimFuzz achieves up to 73.22% multiplexer coverage on the high-quality seed corpus. Our findings highlight critical security bugs in mainstream RISC-V processors and offer actionable insights for improving functional verification.