mlsanityData to 14 Oct 2026

Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text

arXiv 2610.14378 ↗First posted 9 Oct 2026cs.CR cs.CLcited by 0

On the map: LLM safety and security → Watermarking generated content and models

Abstract (arXiv)

We introduce Tokenization-Invariant Watermarking (TI-WM), a method that embeds a watermark during token generation but keys and scores it on canonicalized UTF-8 bytes. The detector does not invoke the tokenizer. Canonicalizer-preserving edits leave its score unchanged; other localized canonical-byte edits affect a bounded number of scoring windows. Across three fixed keys and 128 prompts per key and model, a deterministic white-box attack at a 5% word-edit budget leaves mean attacked-AUC 0.530 on Qwen3-8B and 0.664 on Gemma 4 E4B, compared with 0.410 and 0.489 for Unigram with detection-side deletion. These operating points are not jointly matched on quality and clean detectability. Bias sweeps show model-dependent distortion costs, and shared-rule controls support a locality interpretation without establishing a unique advantage of bytes. Visible insertion outside the canonicalizer's deletion set defeats the default design. Alphabetic filtering closes that channel but does not resolve source shift or calibrated detection power. TI-WM thus provides a concrete construction with local scoring guarantees and measured attack-ranking gains, not a general robustness or low-false-positive-rate deployment guarantee.

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 18 of its 26 references that are in the map. First posted on arXiv 9 Oct 2026.

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18 of 26 references.

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  1. 2026MarkSec: Capability-Aware Evaluation of Adversarial Attacks Against LLM Watermarks15 Sep
  2. 2026LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization11 Jul
  3. 2026PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks9 May
  4. 2025DITTO: A Spoofing Attack Framework on Watermarked LLMs via Knowledge Distillation13 Oct
  5. 2025Character-Level Perturbations Disrupt LLM Watermarks11 Sep
  6. 2025Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models28 Aug
  7. 2024Robust Detection of Watermarks for Large Language Models Under Human Edits21 Nov
  8. 2024Watermark under Fire: A Robustness Evaluation of LLM Watermarking20 Nov
  9. 2024A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules1 Apr
  10. 2024Watermark Stealing in Large Language Models29 Feb
  11. 2023Mark My Words: Analyzing and Evaluating Language Model Watermarks1 Dec
  12. 2023Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models7 Nov
6 more
  1. 2023A Semantic Invariant Robust Watermark for Large Language Models10 Oct
  2. 2023Robust Distortion-free Watermarks for Language Models28 Jul
  3. 2023Three Bricks to Consolidate Watermarks for Large Language Models26 Jul
  4. 2023Provable Robust Watermarking for AI-Generated Text30 Jun
  5. 2023On the Reliability of Watermarks for Large Language Models7 Jun
  6. 2023A Watermark for Large Language Models24 Jan

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  1. 2026ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models
  2. 2026ChainMark: Model-Free LLM Watermarking with Closed-Form Calibration
  3. 2026RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models
  4. 2024PostMark: A Robust Blackbox Watermark for Large Language Models
  5. 2026ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport
  6. 2024Black-Box Detection of Language Model Watermarks
  7. 2026XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts
  8. 2026Anchor-ECC: Local Integrity Checking for Watermarked LLM Outputs via Error-Correcting Codes