Artificial Intelligence Risk Management Framework (AI RMF 1.0) - Japanese translation
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Documented incidents involving compromised machine learning libraries, malicious PyPI packages, and insecure model-serving or distribution pathways indicate that AI supply chain attacks are no longer theoretical and represent a growing class of adversarial activity targeting commercial and government systems.
“Documented incidents involving compromised machine learning libraries, malicious PyPI packages, and insecure model-serving or distribution pathways indicate that AI supply chain attacks are no longer theoretical; they represent a growing class of adversarial activity targeting both commercial and government systems [1-3].”
From AI Supply Chain Security: MBOM-PQC Provenance, PQC Attestation, and a Maturity Model for Quantum-Resistant Assurance (2026) · cites this paper as [1]
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Experimental — accuracy not yet measuredPre-trained models and supporting repositories can be tampered with during distribution, and dependency ecosystems such as PyPI and NPM can be abused to deliver malicious ML libraries.
“Documented incidents and security research indicate that pre-trained models and supporting repositories can be tampered with during distribution, and that dependency ecosystems such as PyPI and NPM can be abused to deliver malicious ML libraries [1,2,22].”
From AI Supply Chain Security: MBOM-PQC Provenance, PQC Attestation, and a Maturity Model for Quantum-Resistant Assurance (2026) · cites this paper as [1]
The risk-measurement discussion (p. 7) says third-party software and data can introduce risks, and Appendix B (p. 41) discusses risks associated with pre-trained models. Neither passage addresses tampering during distribution or malicious ML libraries delivered through PyPI or NPM; searches for those terms and for model tampering found no supporting passage in our copy. (LLM, conf=0.72)
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Research works printed but not linked: 1
- 、AI 、 容易に更 。 本書は https://doi.org/10.6028/NIST.AI.100-1 から無料で入手できます。 · DOI
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Sorted by citations
Jaffal, N. O., Alkhanafseh, M., & Mohaisen, D. (2025) · AI · doi:10.3390/ai6090216 · Cited by 69 · see citing works
Three lines of defense against risks from AI
Schuett, J. (2025) · AI & SOCIETY · doi:10.1007/s00146-023-01811-0 · Cited by 41 · see citing works
Navigating accountability: the role of paradata in AI documentation and governance
Cameron, S., Franks, P. C., Huvila, I., & Mooradian, N. (2025) · Journal of Documentation · doi:10.1108/jd-01-2025-0009 · Cited by 6 · see citing works
Campbell, R. (2026) · Systems · doi:10.3390/systems14050593 · Cited by 3 · see citing works
Hoang, V. T., & Nguyen, N. M. (2026) · Digital Policy, Regulation and Governance · doi:10.1108/dprg-05-2026-0286
- Authors
- NIST, G. M.
- Corresponding author
- NIST, Gaithersburg MD
- Published
- 2024
- OpenAlex
- W4400941260
- Cited by
- 269
- References
- 0
- Citations by year
- 2026: 161 · 2025: 80 · 2024: 24 · 2023: 4
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Funders
- Information Technology Laboratory
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- AI risk management framework, Japanese translation, AI governance, risk assessment
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