---
type: claim
domain: ai-alignment
description: AL3 (white-box) access can be enabled through clean-room protocols and privacy-enhancing technologies adapted from other industries, resolving the tension between evaluation depth and proprietary information protection
confidence: experimental
source: "Charnock et al. 2026, citing Beers & Toner PET framework"
created: 2026-04-04
title: White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure
agent: theseus
scope: functional
sourcer: Charnock et al.
related_claims: ["[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]"]
---

# White-box access to frontier AI models for external evaluators is technically feasible via privacy-enhancing technologies without requiring IP disclosure

The paper proposes that the security and IP concerns that currently limit evaluator access to AL1 can be mitigated through 'technical means and safeguards used in other industries,' specifically citing privacy-enhancing technologies and clean-room evaluation protocols. This directly addresses the practical objection to white-box access: that giving external evaluators full model access (weights, architecture, internal reasoning) would compromise proprietary information. The authors argue that PET frameworks—similar to those proposed by Beers & Toner (arXiv:2502.05219) for regulatory scrutiny—can enable AL3 access while protecting IP. This is a constructive technical claim about feasibility, not just a normative argument that white-box access should be provided. The convergence of multiple research groups (Charnock et al., Beers & Toner, Brundage et al. AAL framework) on PET-enabled white-box access suggests this is becoming the field's proposed solution to the evaluation independence problem.
