Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking Across Datasets, Models, and Generated Content

Abstract

Large language models (LLMs) require substantial investments and are increasingly deployed in high-stakes domains, making it critical to protect LLM-related assets and to trace their provenance. Identity technologies such as fingerprinting and watermarking address these needs by enabling ownership verification and attribution, and have rapidly emerged as an active research focus. However, existing techniques lack a systematic organisation, leading to two key issues, terminological confusion and isolated research lines, that have hindered the development of this research field. To this end, we present a comprehensive review of LLM identity techniques, focusing on fingerprinting and watermarking across the LLM lifecycle, including datasets, models, and generated content. We make three primary contributions. First, we introduce implicit identity (Implicit-ID for short) as a unifying abstraction and distinguish fingerprinting from watermarking. Second, we propose a lifecycle-based taxonomy that organises techniques by asset type and verification role, aligning each with asset protection or provenance.

Third, we establish an evaluation framework around three objectives---identifiability, robustness, and deployability. Together, these contributions structure the landscape of LLM identity techniques, clarify terminology, and highlight directions toward secure deployment.