# SetPrint: Black-Box LLM Fingerprinting via Setwise Correspondence of Response Changes — arXiv [Canonical reading](https://escapemeridian.com/readings/setprint-black-box-llm-fingerprinting-via-setwise-b822626d) The authors of SetPrint are Jinmo Han, Ju Yeon Kang, Jimin Hong, Sunghwan Ahn, Yonghyeon Jun and Nam Soo Kim. They introduce a training-free method for black-box LLM fingerprinting: assessing whether a target model was derived from a source model using only their responses to queries. The method perturbs queries and compares the resulting response changes. It represents each change relative to the mean change in its own set, then compares changes from the two models caused by the same perturbation. The authors say this combines relations among changes within each model’s set with correspondence between the sets, which existing approaches have not fully exploited. On LeaFBench, they report that SetPrint consistently outperforms existing training-free black-box fingerprinting methods, and attribute the gains to both kinds of comparison. The abstract does not give performance figures or describe the benchmark’s scope, so it leaves open how the reported advantage varies across test conditions. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Nov 7, 2026 (2026-11-07). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-11T19:47:40.046Z. First observed: 2026-11-11T05:06:49.863Z. Retrieved: 2026-11-11T19:36:46.728Z. - [SetPrint: Black-Box LLM Fingerprinting via Setwise Correspondence of Response Changes](https://arxiv.org/abs/2611.12950) ## Limits The supplied evidence is the arXiv abstract; it does not provide performance figures or describe LeaFBench’s scope or test conditions. ## Open question How does SetPrint’s reported advantage on LeaFBench vary across test conditions? ## Source-stated The authors introduce SetPrint, a training-free method for determining whether a target model was derived from a source model using observable responses to input queries. > Black-box LLM fingerprinting aims to determine whether a target model was derived from a source model when only the output responses to input queries are observable. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source-stated SetPrint represents each response change relative to the mean change in its set and compares changes induced by the same perturbation across the two models. > SetPrint expresses each response change relative to the mean change of its own set, and then compares changes from the two models that were induced by the same perturbation. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source-stated The authors report that SetPrint consistently outperforms existing training-free black-box fingerprinting methods on LeaFBench. > On LeaFBench, SetPrint consistently outperforms existing training-free black-box fingerprinting methods. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source-stated The authors attribute SetPrint’s gains to both within-set relations and between-set correspondence. > Further analyses attribute these gains to both within-set relations and between-set correspondence. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source and contributor context Han, Jinmo, Kang, Ju Yeon, Hong, Jimin, Ahn, Sunghwan, Jun, Yonghyeon, Kim, Nam Soo · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: b9c83b8e-fffb-47fe-8e7c-b71b251ea30d. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-11T19:47:40.046Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: b32e7ca19eba7d61972ecda547df69a7b592f55ba125765d2bd089a121ac34ba. Source text hash: 920a849f4d5a3c6075b0a1d3e95c87d5fd9c845367c186d1dd77e802952dae77. ## Revision and source-check history ### Version 1 — 2026-11-11T19:47:40.046Z The authors of SetPrint are Jinmo Han, Ju Yeon Kang, Jimin Hong, Sunghwan Ahn, Yonghyeon Jun and Nam Soo Kim. They introduce a training-free method for black-box LLM fingerprinting: assessing whether a target model was derived from a source model using only their responses to queries. The method perturbs queries and compares the resulting response changes. It represents each change relative to the mean change in its own set, then compares changes from the two models caused by the same perturbation. The authors say this combines relations among changes within each model’s set with correspondence between the sets, which existing approaches have not fully exploited. On LeaFBench, they report that SetPrint consistently outperforms existing training-free black-box fingerprinting methods, and attribute the gains to both kinds of comparison. The abstract does not give performance figures or describe the benchmark’s scope, so it leaves open how the reported advantage varies across test conditions. ## Original source arXiv cs.AI · Article Original source date: Nov 7, 2026 (2026-11-07). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-11T19:47:40.046Z. First observed: 2026-11-11T05:06:49.863Z. Retrieved: 2026-11-11T19:36:46.728Z. - [SetPrint: Black-Box LLM Fingerprinting via Setwise Correspondence of Response Changes](https://arxiv.org/abs/2611.12950) ## Limits The supplied evidence is the arXiv abstract; it does not provide performance figures or describe LeaFBench’s scope or test conditions. ## Open question How does SetPrint’s reported advantage on LeaFBench vary across test conditions? ## Source-stated The authors introduce SetPrint, a training-free method for determining whether a target model was derived from a source model using observable responses to input queries. > Black-box LLM fingerprinting aims to determine whether a target model was derived from a source model when only the output responses to input queries are observable. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source-stated SetPrint represents each response change relative to the mean change in its set and compares changes induced by the same perturbation across the two models. > SetPrint expresses each response change relative to the mean change of its own set, and then compares changes from the two models that were induced by the same perturbation. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source-stated The authors report that SetPrint consistently outperforms existing training-free black-box fingerprinting methods on LeaFBench. > On LeaFBench, SetPrint consistently outperforms existing training-free black-box fingerprinting methods. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source-stated The authors attribute SetPrint’s gains to both within-set relations and between-set correspondence. > Further analyses attribute these gains to both within-set relations and between-set correspondence. - [Supporting source](https://arxiv.org/abs/2611.12950) ## Source and contributor context Han, Jinmo, Kang, Ju Yeon, Hong, Jimin, Ahn, Sunghwan, Jun, Yonghyeon, Kim, Nam Soo · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: b9c83b8e-fffb-47fe-8e7c-b71b251ea30d. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-11T19:47:40.046Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: b32e7ca19eba7d61972ecda547df69a7b592f55ba125765d2bd089a121ac34ba. Source text hash: 920a849f4d5a3c6075b0a1d3e95c87d5fd9c845367c186d1dd77e802952dae77. ### passed — 2026-11-11T19:47:40.046Z — anthropic/claude-opus-5.5 Publication: b9c83b8e-fffb-47fe-8e7c-b71b251ea30d attribution — pass: The reading names the six listed authors correctly, attributes the method to them, and frames the benchmark results as their report ('they report', 'attribute'), not as established fact. > Authors:Jinmo Han, Ju Yeon Kang, Jimin Hong, Sunghwan Ahn, Yonghyeon Jun, Nam Soo Kim summary — pass: The reading leads with the development and explains black-box LLM fingerprinting in plain language: telling whether one model was derived from another using only its responses. It then describes the mechanism and the reported result. LeaFBench and training-free are not explained in depth, but the reading identifies LeaFBench as a benchmark, and neither term blocks understanding. The abstract gives no wider stakes, and the reading does not invent any. The question follows from a real gap in the evidence. Optional term-library tooltip suggestions: training-free, perturbation. > Black-box LLM fingerprinting aims to determine whether a target model was derived from a source model when only the output responses to input queries are observable. date — pass: The originalPublished date of 2026-11-07 matches the v1 submission date on arXiv. It is not the later feed date of 2026-11-11. > \[Submitted on 7 Nov 2026\] support — pass: Each claim is a close paraphrase of the abstract. The stated limitation is accurate: the abstract gives no performance figures or benchmark scope. The reading implies no benefits or costs, and its question does not assume an unsupported outcome. > SetPrint expresses each response change relative to the mean change of its own set, and then compares changes from the two models that were induced by the same perturbation. media — pass: The selected media is the verified direct link to the original arXiv abstract page, which matches the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/b822626dfb6130a299bf2c9f784ec69806d35d9a82a5a2d1bfb2fc76ff2b46fb/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/setprint-black-box-llm-fingerprinting-via-setwise-b822626d.json)