Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments

C. Schnabl, D. Hugenroth, B. Marino, A. R. Beresford. 2025. ICML 2025 Workshop on Technical AI Governance.

Original	https://arxiv.org/abs/2506.23706
DOI	10.48550/arXiv.2506.23706
arXiv	2506.23706
Version	arXiv v1 (2025-06-30) read on 2026-09-23 and 2026-09-25, and Fig. 1, §4 and §5 re-read on 2026-10-17; no later version or archival publication found. The arXiv comment reads "ICML 2024 Workshop TAIG", and the PDF and HTML carry the ICML 2025 template header ("Proceedings of the 42nd International Conference on Machine Learning ... PMLR 267"). The ICML 2025 virtual site (https://icml.cc/virtual/2025/48334) lists it as a workshop poster at the Workshop on Technical AI Governance, and it was not found in the PMLR 267 volume, so it is recorded as a workshop paper, not a main-conference publication.
Accessed	2026-10-20
Authored or published by	University of Cambridge
Imported from	hodgkins-ai-verification-papers@c71e59ff0e8e
Note	Listed under "Trusted execution and attestation" in the Hodgkins bibliography (CC BY 4.0).

Cited by