# StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models

arXiv: https://arxiv.org/abs/2506.05502 · HTML: https://mlsanity.com/paper/2506.05502/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 5 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, 23 of 23)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023)
- [Provable Robust Watermarking for AI-Generated Text](https://mlsanity.com/paper/2306.17439/) (2023)
- [On the Reliability of Watermarks for Large Language Models](https://mlsanity.com/paper/2306.04634/) (2023)
- [Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding](https://mlsanity.com/paper/2009.03015/) (2020)
- [Unbiased Watermark for Large Language Models](https://mlsanity.com/paper/2310.10669/) (2023)
- [Watermark Stealing in Large Language Models](https://mlsanity.com/paper/2402.19361/) (2024)
- [Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models](https://mlsanity.com/paper/2311.04378/) (2023)
- [Three Bricks to Consolidate Watermarks for Large Language Models](https://mlsanity.com/paper/2308.00113/) (2023)
- [REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models](https://mlsanity.com/paper/2310.12362/) (2023)
- [Mark My Words: Analyzing and Evaluating Language Model Watermarks](https://mlsanity.com/paper/2312.00273/) (2023)
- [Advancing Beyond Identification: Multi-bit Watermark for Large Language Models](https://mlsanity.com/paper/2308.00221/) (2023)
- [Towards Codable Watermarking for Injecting Multi-bits Information to LLMs](https://mlsanity.com/paper/2307.15992/) (2023)
- [Excuse me, sir? Your language model is leaking (information)](https://mlsanity.com/paper/2401.10360/) (2024)
- [Multi-Bit Distortion-Free Watermarking for Large Language Models](https://mlsanity.com/paper/2402.16578/) (2024)

## Built on by (most cited first, 12 of 12)

- [Majority Bit-Aware Watermarking For Large Language Models](https://mlsanity.com/paper/2508.03829/) (2025)
- [MirrorMark: Generalizable Mirrored Sampling for Multi-bit LLM Watermarking](https://mlsanity.com/paper/2601.22246/) (2026)
- [ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport](https://mlsanity.com/paper/2602.07235/) (2026)
- [QuantileMark: A Message-Symmetric Multi-bit Watermark for LLMs](https://mlsanity.com/paper/2604.13786/) (2026)
- [Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark](https://mlsanity.com/paper/2605.11653/) (2026)
- [Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation](https://mlsanity.com/paper/2607.05354/) (2026)
- [WorldCup Sampling for Multi-bit LLM Watermarking](https://mlsanity.com/paper/2602.01752/) (2026)
- [TensorCommitments: A Lightweight Verifiable Inference for Language Models](https://mlsanity.com/paper/2602.12630/) (2026)
- [Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints](https://mlsanity.com/paper/2604.08759/) (2026)
- [TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC](https://mlsanity.com/paper/2604.12216/) (2026)
- [WeaveMark: Robust and Scalable Multi-bit LLM Watermarking via Coded Payload Spreading](https://mlsanity.com/paper/2609.02177/) (2026)
- [SimpleMark: Fast Multi-Bit Text Watermarking under f -Divergence Constraints](https://mlsanity.com/paper/2610.05712/) (2026)

## Abstract (arXiv)

Watermarking for large language models (LLMs) offers a promising approach to identifying AI-generated text. Existing approaches, however, either compromise the distribution of original generated text by LLMs or are limited to embedding zero-bit information that only allows for watermark detection but ignores identification. We present StealthInk, a stealthy multi-bit watermarking scheme that preserves the original text distribution while enabling the embedding of provenance data, such as userID, TimeStamp, and modelID, within LLM-generated text. This enhances fast traceability without requiring access to the language model's API or prompts. We derive a lower bound on the number of tokens necessary for watermark detection at a fixed equal error rate, which provides insights on how to enhance the capacity. Comprehensive empirical evaluations across diverse tasks highlight the stealthiness, detectability, and resilience of StealthInk, establishing it as an effective solution for LLM watermarking applications.