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

Abstract

Large language models (LLMs) are substantial investments and 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, as the field remains at an early stage, existing techniques lack a systematic organization, leading to two key challenges—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 \emph{implicit identity} 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---and summarise representative metrics under realistic access and transformation regimes, providing a common basis for comparison. Together, these contributions unify and structure the landscape of LLM identity techniques, clarify terminology, and highlight directions toward more secure deployment.