# AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Training

arXiv: https://arxiv.org/abs/2601.05703 · HTML: https://mlsanity.com/paper/2601.05703/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?](https://mlsanity.com/paper/2404.12691/) (2024)
- [Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations](https://mlsanity.com/paper/2406.17548/) (2024)
- [Atlas: A Framework for ML Lifecycle Provenance & Transparency](https://mlsanity.com/paper/2502.19567/) (2025)
- [BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems](https://mlsanity.com/paper/2309.12206/) (2023)
- [Implementing AI Bill of Materials (AI BOM) with SPDX 3.0: A Comprehensive Guide to Creating AI and Dataset Bill of Materials](https://mlsanity.com/paper/2504.16743/) (2025)

## Built on by (most cited first, 6 of 6)

- [Towards Security-Auditable LLM Agents: A Unified Graph Representation](https://mlsanity.com/paper/2605.06812/) (2026)
- [AgentRiskBOM: A Risk-Scoping Security Bill of Materials for Agentic AI Systems](https://mlsanity.com/paper/2606.21877/) (2026)
- [AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programs](https://mlsanity.com/paper/2607.01640/) (2026)
- [A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models](https://mlsanity.com/paper/2607.17242/) (2026)
- [AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration](https://mlsanity.com/paper/2607.26652/) (2026)
- [A Security-Oriented Lifecycle Model for Large Language Model Systems](https://mlsanity.com/paper/2608.03626/) (2026)

## Abstract (arXiv)

The rapid adoption of complex AI systems has outpaced the development of tools to ensure their transparency, security, and regulatory compliance. In this paper, the AI Bill of Materials (AIBOM), an extension of the Software Bill of Materials (SBOM), is introduced as a standardized, verifiable record of trained AI models and their environments. Our proof-of-concept platform, AIBoMGen, automates the generation of signed AIBOMs by capturing datasets, model metadata, and environment details during training. The training platform acts as a neutral, third-party observer and root of trust. It enforces verifiable AIBOM creation for every job. The system uses cryptographic hashing, digital signatures, and in-toto attestations to ensure integrity and protect against threats such as artifact tampering by dishonest model creators. Our evaluation demonstrates that AIBoMGen reliably detects unauthorized modifications to all artifacts and can generate AIBOMs with negligible performance overhead. These results highlight the potential of AIBoMGen as a foundational step toward building secure and transparent AI ecosystems, enabling compliance with regulatory frameworks like the EUs AI Act.