Trust in Software Supply Chains: Blockchain-Enabled SBOM and the AIBOM Future
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Abstract (arXiv)
The robustness of critical infrastructure systems is contingent upon the integrity and transparency of their software supply chains. A Software Bill of Materials (SBOM) is pivotal in this regard, offering an exhaustive inventory of components and dependencies crucial to software development. However, prevalent challenges in SBOM sharing, such as data tampering risks and vendors' reluctance to fully disclose sensitive information, significantly hinder its effective implementation. These challenges pose a notable threat to the security of critical infrastructure and systems where transparency and trust are paramount, underscoring the need for a more secure and flexible mechanism for SBOM sharing. To bridge the gap, this study introduces a blockchain-empowered architecture for SBOM sharing, leveraging verifiable credentials to allow for selective disclosure. This strategy not only heightens security but also offers flexibility. Furthermore, this paper broadens the remit of SBOM to encompass AI systems, thereby coining the term AI Bill of Materials (AIBOM). The advent of AI and its application in critical infrastructure necessitates a nuanced understanding of AI software components, including their origins and interdependencies. The evaluation of our solution indicates the feasibility and flexibility of the proposed SBOM sharing mechanism, positing a solution for safeguarding (AI) software supply chains, which is essential for the resilience and reliability of modern critical infrastructure systems.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Jul 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 1.
8 papers.
- 2023Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agentscited by 14
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboardscited by 6
- 2025An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Facecited by 6
- 2024Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approachcited by 1
- 2025Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?cited by 1
- 2025What Makes Code Generation Ethically Sourced?cited by 0
- 2025AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agentscited by 0
- 2026AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integrationcited by 0
- 2026AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration29 Jul 2026
- 2025Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?29 Dec 2025
- 2025AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agents3 Oct 2025
- 2025What Makes Code Generation Ethically Sourced?26 Jul 2025
- 2025An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face6 Feb 2025
- 2024Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approach16 Aug 2024
- 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards4 Jul 2024
- 2023Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents22 Nov 2023