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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2601.11699 |
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| author | Brundage, Miles Dreksler, Noemi Homewood, Aidan McGregor, Sean Paskov, Patricia Stosz, Conrad Sastry, Girish Cooper, A. Feder Balston, George Adler, Steven Casper, Stephen Anderljung, Markus Werner, Grace Mindermann, Soren Mavroudis, Vasilios Bucknall, Ben Stix, Charlotte Freund, Jonas Pacchiardi, Lorenzo Hernandez-Orallo, Jose Pistillo, Matteo Chen, Michael Painter, Chris Ball, Dean W. O'Keefe, Cullen Weil, Gabriel Harack, Ben Finley, Graeme Hassan, Ryan Emmons, Scott Foster, Charles Reuel, Anka Treece, Bri Bengio, Yoshua Reti, Daniel Bommasani, Rishi Trout, Cristian Shamsabadi, Ali Shahin Dattani, Rajiv Weller, Adrian Trager, Robert Sevilla, Jaime Wagner, Lauren Soder, Lisa Ramakrishnan, Ketan Papadatos, Henry Murray, Malcolm Tovcimak, Ryan |
| author_facet | Brundage, Miles Dreksler, Noemi Homewood, Aidan McGregor, Sean Paskov, Patricia Stosz, Conrad Sastry, Girish Cooper, A. Feder Balston, George Adler, Steven Casper, Stephen Anderljung, Markus Werner, Grace Mindermann, Soren Mavroudis, Vasilios Bucknall, Ben Stix, Charlotte Freund, Jonas Pacchiardi, Lorenzo Hernandez-Orallo, Jose Pistillo, Matteo Chen, Michael Painter, Chris Ball, Dean W. O'Keefe, Cullen Weil, Gabriel Harack, Ben Finley, Graeme Hassan, Ryan Emmons, Scott Foster, Charles Reuel, Anka Treece, Bri Bengio, Yoshua Reti, Daniel Bommasani, Rishi Trout, Cristian Shamsabadi, Ali Shahin Dattani, Rajiv Weller, Adrian Trager, Robert Sevilla, Jaime Wagner, Lauren Soder, Lisa Ramakrishnan, Ketan Papadatos, Henry Murray, Malcolm Tovcimak, Ryan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11699 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies Brundage, Miles Dreksler, Noemi Homewood, Aidan McGregor, Sean Paskov, Patricia Stosz, Conrad Sastry, Girish Cooper, A. Feder Balston, George Adler, Steven Casper, Stephen Anderljung, Markus Werner, Grace Mindermann, Soren Mavroudis, Vasilios Bucknall, Ben Stix, Charlotte Freund, Jonas Pacchiardi, Lorenzo Hernandez-Orallo, Jose Pistillo, Matteo Chen, Michael Painter, Chris Ball, Dean W. O'Keefe, Cullen Weil, Gabriel Harack, Ben Finley, Graeme Hassan, Ryan Emmons, Scott Foster, Charles Reuel, Anka Treece, Bri Bengio, Yoshua Reti, Daniel Bommasani, Rishi Trout, Cristian Shamsabadi, Ali Shahin Dattani, Rajiv Weller, Adrian Trager, Robert Sevilla, Jaime Wagner, Lauren Soder, Lisa Ramakrishnan, Ketan Papadatos, Henry Murray, Malcolm Tovcimak, Ryan Computers and Society 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. |
| title | Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2601.11699 |