# A Nested Watermark for Large Language Models

arXiv: https://arxiv.org/abs/2506.17308 · HTML: https://mlsanity.com/paper/2506.17308/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 6 of 6)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Automatic Detection of Machine Generated Text: A Critical Survey](https://mlsanity.com/paper/2011.01314/) (2020)

## Abstract (arXiv)

The rapid advancement of large language models (LLMs) has raised concerns regarding their potential misuse, particularly in generating fake news and misinformation. To address these risks, watermarking techniques for autoregressive language models have emerged as a promising means for detecting LLM-generated text. Existing methods typically embed a watermark by increasing the probabilities of tokens within a group selected according to a single secret key. However, this approach suffers from a critical limitation: if the key is leaked, it becomes impossible to trace the text's provenance or attribute authorship. To overcome this vulnerability, we propose a novel nested watermarking scheme that embeds two distinct watermarks into the generated text using two independent keys. This design enables reliable authorship identification even in the event that one key is compromised. Experimental results demonstrate that our method achieves high detection accuracy for both watermarks while maintaining the fluency and overall quality of the generated text.