mlsanityData to 14 Oct 2026

Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework

arXiv 2603.14968 ↗First posted 16 Mar 2026cs.CR cs.CLcited by 0

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

Abstract (arXiv)

While watermarking serves as a critical mechanism for LLM provenance, existing secret-key schemes tightly couple detection with injection, requiring access to keys or provider-side scheme-specific detectors for verification. This dependency creates a fundamental barrier for real-world governance, as independent auditing becomes impossible without compromising model security or relying on the opaque claims of service providers. To resolve this dilemma, we introduce TTP-Detect, a pioneering black-box framework designed for non-intrusive, third-party watermark verification. By decoupling detection from injection, TTP-Detect reframes verification as a relative hypothesis testing problem. It employs a proxy model to amplify watermark-relevant signals and a suite of complementary relative measurements to assess the alignment of the query text with watermarked distributions. Extensive experiments across representative watermarking schemes, datasets and models demonstrate that TTP-Detect achieves superior detection performance and robustness against diverse attacks.

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 14 of its 26 references that are in the map. First posted on arXiv 16 Mar 2026.

Builds on

14 of 26 references.

Closest ancestors · 10

Same direction, newest first

  1. 2024MarkLLM: An Open-Source Toolkit for LLM Watermarking16 May
  2. 2024An Entropy-based Text Watermarking Detection Method20 Mar
  3. 2024GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick20 Feb
  4. 2024k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text17 Feb
  5. 2023On the Learnability of Watermarks for Language Models7 Dec
  6. 2023A Robust Semantics-based Watermark for Large Language Model against Paraphrasing15 Nov
  7. 2023Robust Distortion-free Watermarks for Language Models28 Jul
  8. 2023Provable Robust Watermarking for AI-Generated Text30 Jun
  9. 2023Who Wrote this Code? Watermarking for Code Generation24 May
  10. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense23 Mar

Other references · 4

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  3. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  4. 2019ELI5: Long Form Question Answeringcited by 575

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  1. 2026Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks
  2. 2025PVMark: Enabling Public Verifiability for LLM Watermarking Schemes
  3. 2024Black-Box Detection of Language Model Watermarks
  4. 2026LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization
  5. 2026AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks
  6. 2024Proving membership in LLM pretraining data via data watermarks
  7. 2026VOW: Verifiable and Oblivious Watermark Detection for Large Language Models
  8. 2025Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark