Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content
arXiv:2605.29245v1 Announce Type: cross Abstract: This paper presents a survey and taxonomy of LLM fingerprinting and watermarking for identity, ownership verification, provenance, and generated-content attribution. Large language models (LLMs) require substantial investments in data, computation, and expertise, and are increasingly deployed in high-stakes settings, making it critical to protect LLM-related assets and trace their origins. Existing work has rapidly expanded across dataset provenance, model ownership, and generated-content detection, but the field remains fragmented: fingerprint
The rapid deployment of LLMs and increasing investments into their development necessitate robust mechanisms for asset protection and content attribution.
Protecting intellectual property, ensuring authenticity, and maintaining trust in AI-generated content are becoming critical for industry and national security.
The focus is shifting towards foundational identity technologies for LLMs, moving beyond mere content detection to encompass model ownership and data provenance.
- · AI IP owners
- · Cybersecurity firms
- · Platform providers
- · Regulatory bodies
- · Malicious actors
- · Intellectual property infringers
- · Producers of unchecked AI content
Widespread adoption of fingerprinting and watermarking techniques will enhance the security and trustworthiness of LLM ecosystems.
This could lead to new legal frameworks and international standards for AI accountability and content authenticity.
The ability to definitively attribute AI creations might accelerate the integration of LLMs into highly sensitive applications, while also creating new forms of digital rights management.
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Read at arXiv cs.LG