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This research investigates the robustness of large language model fingerprints, demonstrating that model-specific traces persist even in distilled variants and LoRA adaptations when surface cues are suppressed. The study identifies semantic representations as the most reliable signal for attribution across different languages and prompt distributions, offering critical insights into the limits of model anonymization.
Every Model Leaves a Trace: Multi-Resolution LLM Fingerprinting
arXiv:2610.28434v1 Announce Type: new Abstract: Large language models leave model-specific traces in their generated text, but it remains unclear how finely these fingerprints can distinguish related models and which signals remain informative as obvious cues are reduced. We study this through fingerprint resolution, considering both model resolution, from different families to distilled and adapted variants, and signal resolution across behavioral, stylometric, lexical, and semantic evidence. We progressively suppress surface cues, paraphrase outputs, vary the amount of available text, and test generalization across prompt distributions and languages. Our results show that fingerprints persist beyond broad family differences, remaining detectable in distilled models and even LoRA adaptations of the same base model. Although attribution weakens as surface and lexical cues are altered, substantial model-specific signal remains. These signals transfer across prompt sources and languages, with semantic representations proving most robust across languages. These findings show that model identity is recoverable at multiple resolutions, but that recoverability depends on model similarity and the evidence available in the text.
Abstract:Large language models leave model-specific traces in their generated text, but it remains unclear how finely these fingerprints can distinguish related models and which signals remain informative as obvious cues are reduced. We study this through fingerprint resolution, considering both model resolution, from different families to distilled and adapted variants, and signal resolution across behavioral, stylometric, lexical, and semantic evidence. We progressively suppress surface cues, paraphrase outputs, vary the amount of available text, and test generalization across prompt distributions and languages. Our results show that fingerprints persist beyond broad family differences, remaining detectable in distilled models and even LoRA adaptations of the same base model. Although attribution weakens as surface and lexical cues are altered, substantial model-specific signal remains. These signals transfer across prompt sources and languages, with semantic representations proving most robust across languages. These findings show that model identity is recoverable at multiple resolutions, but that recoverability depends on model similarity and the evidence available in the text.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.28434 [cs.CL]
(or arXiv:2610.28434v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.28434 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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