Token-Efficient Change Detection in LLM APIs

Abstract:Remote change detection in LLMs is a difficult problem. Existing methods are either too expensive for deployment at scale, or require initial white-box access to model weights or grey-box access to log probabilities. We aim to achieve both low cost and strict black-box operation, observing only output tokens. Our approach hinges on specific inputs we call Border Inputs, for which there exists more than one output top token. From a statistical perspective, optimal change detection depends on the model's Jacobian and the Fisher information of the output distribution. Analyzing these quantities in low-temperature regimes shows that border inputs enable powerful change detection tests. Building on this insight, we propose the Black-Box Border Input Tracking (B3IT) scheme. Extensive in-vivo and in-vitro experiments show that border inputs are easily found for non-reasoning tested endpoints, and achieve performance on par with the best available grey-box approaches. B3IT reduces costs by $30\times$ compared to existing methods, while operating in a strict black-box setting.

Machine Learning (cs.LG); Cryptography and Security (cs.CR)

arXiv:2602.11083 [cs.LG]

(or arXiv:2602.11083v4 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.11083 Focus to learn more arXiv-issued DOI via DataCite

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