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

2026-06-06

AI Summary

Black-box large language models often produce censored or misleading outputs on contentious topics due to undisclosed proprietary alignment mechanisms, yet existing approaches struggle to systematically detect such behaviors. This work proposes the first black-box auditing framework that requires no ground-truth labels and instead relies solely on relative behavioral deviations. By mapping responses from a target model and a reference model into a shared semantic space, the method leverages semantic embeddings, behavioral contrast, and statistical significance testing to quantify response bias. Crucially, it does not depend on absolute correctness criteria, is scalable, and has been successfully applied to multiple previously intractable cases. The approach establishes a novel, systematic, and empirically viable paradigm for externally auditing provider-specific alignment behaviors in large language models.

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.

Problem

• proprietary alignment
• large language models
• black-box auditing
• behavioral divergence
• model transparency

Innovation

• proprietary alignment
• black-box auditing
• comparative behavioral analysis
• statistical framework
• large language models