[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85816-en":3,"doc-seo-85816-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85816,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Survey on LLM Watermarking Theory and Deployment","Large language models increasingly power high-impact workflows, but their fluent, scalable text generation raises provenance ambiguity, model misuse, and content laundering concerns. LLM watermarking embeds invisible signatures into outputs to support attribution, auditing, and trust decisions. The literature has expanded rapidly yet in uneven, sometimes orthogonal design directions, limiting method comparison and deployment guidance. This survey organizes watermarking by embedding location, detection authority, assumptions, and targeted threats, then analyzes security–utility trade-offs, attacks, evaluation metrics, and open challenges to guide practical system selection.","A Survey on LLM Watermarking: Theory and Deployment  \nHuy Phan1 , Kieu Dang2 , Ojaswi Dulal2 , Aiham Al Shukairi2 , Abby Shine2 , Chase Garner2 , Phung Lai2  \n1Truman State University, 2University at Albany, State University of New York  \n[fd56851@truman.edu](fd56851@truman.edu)  \narXiv :2607 . 10 103v 1 [ cs .CR] 11 Jul 2026  \nAbstract  \nLarge language models (LLMs) are increasingly embedded in high-impact workflows, yet their ability to generate fluent text at scale has amplified risks of provenance ambiguity, model misuse, and large-scale content laundering. LLM watermarking—embedding invisible signatures into model outputs—has emerged as a promising technical layer for attribution, auditing, and downstream trust decisions. However, the literature has grown rapidly and unevenly: existing categorizations often mix orthogonal design choices, making it difficult to compare methods, reason about guarantees, or translate research results into deployable systems.  \nThis survey provides a systematic, deploymentoriented review of LLM watermarking. We organize the space by the core questions practitioners must answer: where a watermark is embedded (generation-time [vs. training](vs. training)time, token vs. representation), who can detect it (public vs. private detection authority), what is assumed (access to logits, sampling control, secret keys, model ownership), and which threat models are targeted (paraphrasing, translation, summarization, style transfer, token manipulation, and adaptive removal) . We synthesize the main families of techniques—including sampling biasing, codebased schemes, representation- and trainingbased approaches—and analyze their security–utility trade-offs through the lens of detectability, robustness, and distribution shift. We further review attack and evasion strategies, evaluation protocols and metrics (false positive control, calibration, robustness curves), and open challenges such as cross-model transfer, multi-modal pipelines, collusion, and governance constraints. Finally, we provide practical guidance for selecting watermark designs under real operational requirements and identify research directions needed for reliable, accountable LLM deployment.  \n1 Introduction  \nLarge language models (LLMs), such as ChatGPT, Gemini, Claude, and Cohere (Google, 2024 ; OpenAI, 2024 ; Anthropic, 2024 ; Cohere, 2024), have demonstrated remarkable capabilities in text generation, machine translation, and knowledge understanding tasks (Zhang et al., 2023a,c; Xu et al., 2024 ; Hu et al., 2023 ; Kirchenbauer et al., 2023) . They effectively mimic human writing behaviorsand generate complex and coherent outputs from the input text, making it challenging to determine whether a text is authored by humans or generated by LLMs. Due to the high demands of computational resources and human efforts required for training LLMs (Brown et al., 2020 ; Chen et al., 2021), these models are commonly offered as a service through application programming interfaces (APIs), typically requiring users to pay or subscribe (Azure, 2021 ; Bluemix, 2021) . Although users cannot access to the model weights or architectures of these commercial LLMs, this restriction does not ensure the safety of these models. Malicious actors can intentionally mimic cloud-hosted LLM behaviors to offer cheaper services (Wallace et al., 2020 ; Xu et al., 2022) . To conduct this service stealing, an adversary can query a set of inputs through an LLM’s API to retrieve the corresponding outputs. Then, the adversary uses these input-output data to effectively fine-tune their local model. When the number of queries is sufficient to gather enough input-output data within a particular domain, the adversary can steal the cloud-hosted LLM behaviors in that domain (Victor and Efrati, 2023 ; Carliniet al., 2024 ; Conikee, 2024) . Consequently, these concerns underscore significant risks regarding the intellectual property (IP) rights of the cloud-hosted proprietary LLMs (L","cbCaiaodb5Mx1Ocm","https://ap.wps.com/l/cbCaiaodb5Mx1Ocm","pdf",2000744,3,1,17,"English","en",105,"# Abstract\n# 1 Introduction\n## Motivation: LLM risks and IP concerns\n## Watermarking as a practical defense","[{\"question\":\"What problem does LLM watermarking address in large-scale language generation?\",\"answer\":\"It targets provenance ambiguity and misuse by embedding invisible signatures into model outputs, enabling attribution and auditing in workflows where text authenticity is hard to verify.\"},{\"question\":\"How does the survey organize the design space of LLM watermarking?\",\"answer\":\"By core practitioner questions: where the watermark is embedded (generation-time vs. training; token vs. representation), who can detect it (public vs. private authority), what assumptions are made (logits access, sampling control, secret keys, ownership), and which threat models are addressed.\"},{\"question\":\"What kinds of attacks and evaluation aspects does the survey emphasize?\",\"answer\":\"It reviews watermark removal and spoofing strategies, and discusses evaluation protocols and metrics such as false positive control, calibration, robustness curves, along with open challenges like cross-model transfer and multi-modal pipelines.\"}]",1784206434,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-survey-on-llm-watermarking-theory-and-deployment","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/a-survey-on-llm-watermarking-theory-and-deployment/85816/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does LLM watermarking address in large-scale language generation?","Question",{"text":75,"@type":76},"It targets provenance ambiguity and misuse by embedding invisible signatures into model outputs, enabling attribution and auditing in workflows where text authenticity is hard to verify.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the survey organize the design space of LLM watermarking?",{"text":80,"@type":76},"By core practitioner questions: where the watermark is embedded (generation-time vs. training; token vs. representation), who can detect it (public vs. private authority), what assumptions are made (logits access, sampling control, secret keys, ownership), and which threat models are addressed.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of attacks and evaluation aspects does the survey emphasize?",{"text":84,"@type":76},"It reviews watermark removal and spoofing strategies, and discusses evaluation protocols and metrics such as false positive control, calibration, robustness curves, along with open challenges like cross-model transfer and multi-modal pipelines.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]