# Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies

arXiv: https://arxiv.org/abs/2601.11699 · HTML: https://mlsanity.com/paper/2601.11699/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2026.

## Built on by (most cited first, 11 of 11)

- [Measuring AI R&D Automation](https://mlsanity.com/paper/2603.03992/) (2026)
- [Comprehensive AI governance requires addressing non-model gains](https://mlsanity.com/paper/2606.00047/) (2026)
- [What if automating AI R&D triggers an intelligence explosion?](https://mlsanity.com/paper/2609.36054/) (2026)
- [RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies](https://mlsanity.com/paper/2603.11001/) (2026)
- [NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims](https://mlsanity.com/paper/2605.08192/) (2026)
- [Referential Security as a New Paradigm for AI Evaluations](https://mlsanity.com/paper/2605.25673/) (2026)
- [AISPA: User-Centric System Prompt Auditing for Large Language Model Applications](https://mlsanity.com/paper/2607.28617/) (2026)
- [The Normalization of Deviance in AI Development](https://mlsanity.com/paper/2609.05749/) (2026)
- [Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance](https://mlsanity.com/paper/2609.10105/) (2026)
- [Who Audits Whom, on What Substrate, with What Evidence? An Independence-Graded Audit Protocol for Agentic AI](https://mlsanity.com/paper/2609.18272/) (2026)
- [Comprehension Audits to Mitigate Risks from Automated AI Research](https://mlsanity.com/paper/2610.10064/) (2026)

## Abstract (arXiv)

We outline a vision for frontier AI auditing, which we define as rigorous third-party verification of frontier AI developers' safety and security claims, and evaluation of their systems and practices against relevant standards, based on deep, secure access to non-public information. Frontier AI audits should not be limited to a company's publicly deployed products, but should instead consider the full range of organization-level safety and security risks, including internal deployment of AI systems, information security practices, and safety decision-making processes. We describe four AI Assurance Levels (AALs), the higher levels of which provide greater confidence in audit findings. We recommend AAL-1 as a baseline for frontier AI generally, and AAL-2 as a near-term goal for the most advanced subset of frontier AI developers. Achieving the vision we outline will require (1) ensuring high quality standards for frontier AI auditing, so it does not devolve into a checkbox exercise or lag behind changes in the industry; (2) growing the ecosystem of audit providers at a rapid pace without compromising quality; (3) accelerating adoption of frontier AI auditing by clarifying and strengthening incentives; and (4) achieving technical readiness for high AI Assurance Levels so they can be applied when needed.