Token-Efficient Change Detection in LLM APIs
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913171693895680 |
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| author | Chauvin, Timothée Lalanne, Clément Merrer, Erwan Le Loubes, Jean-Michel Taïani, François Tredan, Gilles |
| author_facet | Chauvin, Timothée Lalanne, Clément Merrer, Erwan Le Loubes, Jean-Michel Taïani, François Tredan, Gilles |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11083 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Token-Efficient Change Detection in LLM APIs Chauvin, Timothée Lalanne, Clément Merrer, Erwan Le Loubes, Jean-Michel Taïani, François Tredan, Gilles Machine Learning Cryptography and Security 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. |
| title | Token-Efficient Change Detection in LLM APIs |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2602.11083 |