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

Zhuoshang Wang, Yubing Ren, Yanan Cao, Fang Fang, Xiaoxue Li +1 more
3/16/2026
cs.CRcs.CL

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Abstract

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.

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Cite this paper

@article{wang2026rethinking,
  title  = {Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework},
  author = {Zhuoshang Wang and Yubing Ren and Yanan Cao and Fang Fang and Xiaoxue Li and Li Guo},
  year   = {2026},
  eprint = {2603.14968},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2603.14968v1}
}

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