@inproceedings{cao-etal-2026-breaking,
title = "Breaking the ``Provable Security'': Detecting Finite-Precision Artifacts in {LLM}-based Steganography via Low-Probability Vanishing",
author = "Cao, Wenzhao and
Wang, Yaofei and
Hu, Donghui",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1013/",
doi = "10.18653/v1/2026.findings-acl.1013",
pages = "20262--20274",
ISBN = "979-8-89176-395-1",
abstract = "Recent advances in Large Language Models have fostered a new class of generative linguistic steganography, claim ``provably secure'' by theoretically aligning the steganographic distribution with the language model{'}s natural distribution. We challenge this premise by exposing Low-Probability Vanishing (LPV), an inevitable vulnerability arising from finite-precision arithmetic. To exploit this, we propose RRNs-HT, a novel steganalysis framework based on Representative Random Numbers and Hypothesis Testing, which transforms the detection task from semantic classification to a statistical audit of the sampling mechanism. Crucially, unlike previous work that contrasts machine text against human text, we validate our method in a rigorous homologous setting to strictly isolate sampling artifacts. Experiments demonstrate that RRNs-HT effectively breaks the security of AC and Meteor with high detection accuracy, whereas state-of-the-art semantic steganalyzers degrade to random guessing. Our findings prove that theoretical security is unattainable in practice without addressing finite-precision leakage."
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<abstract>Recent advances in Large Language Models have fostered a new class of generative linguistic steganography, claim “provably secure” by theoretically aligning the steganographic distribution with the language model’s natural distribution. We challenge this premise by exposing Low-Probability Vanishing (LPV), an inevitable vulnerability arising from finite-precision arithmetic. To exploit this, we propose RRNs-HT, a novel steganalysis framework based on Representative Random Numbers and Hypothesis Testing, which transforms the detection task from semantic classification to a statistical audit of the sampling mechanism. Crucially, unlike previous work that contrasts machine text against human text, we validate our method in a rigorous homologous setting to strictly isolate sampling artifacts. Experiments demonstrate that RRNs-HT effectively breaks the security of AC and Meteor with high detection accuracy, whereas state-of-the-art semantic steganalyzers degrade to random guessing. Our findings prove that theoretical security is unattainable in practice without addressing finite-precision leakage.</abstract>
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%0 Conference Proceedings
%T Breaking the “Provable Security”: Detecting Finite-Precision Artifacts in LLM-based Steganography via Low-Probability Vanishing
%A Cao, Wenzhao
%A Wang, Yaofei
%A Hu, Donghui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cao-etal-2026-breaking
%X Recent advances in Large Language Models have fostered a new class of generative linguistic steganography, claim “provably secure” by theoretically aligning the steganographic distribution with the language model’s natural distribution. We challenge this premise by exposing Low-Probability Vanishing (LPV), an inevitable vulnerability arising from finite-precision arithmetic. To exploit this, we propose RRNs-HT, a novel steganalysis framework based on Representative Random Numbers and Hypothesis Testing, which transforms the detection task from semantic classification to a statistical audit of the sampling mechanism. Crucially, unlike previous work that contrasts machine text against human text, we validate our method in a rigorous homologous setting to strictly isolate sampling artifacts. Experiments demonstrate that RRNs-HT effectively breaks the security of AC and Meteor with high detection accuracy, whereas state-of-the-art semantic steganalyzers degrade to random guessing. Our findings prove that theoretical security is unattainable in practice without addressing finite-precision leakage.
%R 10.18653/v1/2026.findings-acl.1013
%U https://aclanthology.org/2026.findings-acl.1013/
%U https://doi.org/10.18653/v1/2026.findings-acl.1013
%P 20262-20274
Markdown (Informal)
[Breaking the "Provable Security": Detecting Finite-Precision Artifacts in LLM-based Steganography via Low-Probability Vanishing](https://aclanthology.org/2026.findings-acl.1013/) (Cao et al., Findings 2026)
ACL