Auditing Proprietary Alignment in Large Language Models: A Comparative Framework Without a Ground-Truth Standard

• URL: http://arxiv.org/abs/2606.08381v1
• Date: Sun, 07 Jun 2026 00:20:55 GMT
• Title: Auditing Proprietary Alignment in Large Language Models: A Comparative Framework Without a Ground-Truth Standard
• Abstract summary: Large language models (LLMs) are increasingly released and deployed through opaque development and deployment pipelines.<n>Various models have been reported to generate responses reflecting proprietary rules and organizational interests.<n>We propose a statistical framework for detecting proprietary alignment in black-box language models via comparative behavioral analysis.
• Score: 19.605143530514777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly released and deployed through opaque development and deployment pipelines, enabling model providers to inject intentional, provider-specific policies without officially announcing them. As a result, various models have been reported to generate responses reflecting proprietary rules and organizational interests, leading to censorship or misinformation on controversial topics. However, systematic identification of such alignment remains a fundamental challenge, complicated by the ambiguity of what ``proprietary'' entails in different contexts. In this paper, we propose a statistical framework for detecting proprietary alignment in black-box language models via comparative behavioral analysis. Our approach quantifies systematic deviations between the responses of a target model and those of a reference set of baseline models in a shared semantic space. By evaluating relative behavioral divergence rather than absolute correctness, our framework enables principled auditing under black-box access. Applied to several widely discussed but previously unquantified cases, it provides a systematic and scalable basis for external assessment of provider-specific alignment behavior in large language models.

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