[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86489-en":3,"doc-seo-86489-105":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},86489,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability","Large language models (LLMs) reshape misinformation from a content-only problem into a broader ecosystem-level security threat. When misused, LLMs can attack not only generated text but also social contexts, evidence sources, retrieval corpora, and verification workflows. The paper proposes a role-layer framework that models LLMs as attackers, defenders, and vulnerable verification components across multiple layers. It organizes LLM-enabled attacks, reviews detection and verification methods, studies vulnerabilities, and outlines countermeasures plus open challenges.","arXiv :2607 . 10402v 1 [ cs .CR] 11 Jul 2026  \nLarge Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability  \nLINGWEI WEI, Institute of Information Engineering, Chinese Academy of Sciences, China and University of Illinois Chicago, USA  \nDOU HU, State Key Laboratory of Media Convergence and Communication, Communication University of China, China  \nWEI ZHOU, Institute of Information Engineering, Chinese Academy of Sciences, China  \nSONGLIN HU, Institute of Information Engineering, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China  \nPHILIP S. YU, University of Illinois Chicago, USA  \nLarge language models (LLMs) have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge. When misused, LLMs create risks beyond false content generation, enabling attacks on the social contexts, evidence sources, retrieval corpora, and verification workflows that misinformation defense depends on. In this paper, we introduce a role-layer framework to unify these risks and defenses. The role dimension characterizes LLMs as attackers, defenders, and vulnerable components of verification systems, while the layer dimension covers content, social contexts, evidence environments, and verification workflows. Guided by this framework, we organize LLM-enabled attacks, investigate LLM-based detection and verification methods, analyze vulnerabilities in LLM-centric detection paradigms, and discuss existing countermeasures against LLM-enabled attacks. Building on this synthesis, we identify three key open challenges: moving from static detection accuracy to budgeted ecosystem-level risk evaluation, hardening LLM-centered verification pipelines against adversarial manipulation, and deploying auditable human-in-the-loop verification systems for trustworthy real-world misinformation defense.  \nCCS Concepts: • Information systems; • Computing methodologies → Artificial intelligence; • Humancentered computing → Human computer interaction (HCI); • Security and privacy → Human and societal aspects of security and privacy;  \n1 Introduction  \nMisinformation, broadly defined as false or misleading information regardless of intent [4, 28, 63, 241], has long posed a major threat to the information ecosystem. The emergence of large language models (LLMs) has substantially reshaped this threat landscape. LLMs lower the cost of producing fluent, coherent, and seemingly authoritative misinformation [61] . They can generate fabricated news articles, misleading summaries, persuasive arguments, false explanations, and localized narratives within seconds. More importantly, LLMs amplify misinformation risks beyond the generation of synthetic false content. Recent studies have also shown that LLMs can be misused to rewrite existing claims to evade detectors [133], personalize narratives for different audiences [124], translate and localize false information across languages [80], simulate personas [50], generate comments [112] and social interactions [107], and assist coordinated manipulation campaigns [26] . As a result, LLMs do not merely increase the volume of misinformation; they also broaden the threat surface by enabling misinformation to be created, amplified, contextualized, and concealed in previous impossible ways.  \nAuthors’ Contact Information: Lingwei Wei, Institute of Information Engineering, Chinese Academy of Sciences, Beijing,, China and University of Illinois Chicago, Chicago, Illinois, USA, [weilingwei@iie.ac.cn](weilingwei@iie.ac.cn); Dou Hu, State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing,, China, [hudou@cuc.edu.cn](hudou@cuc.edu.cn); Wei Zhou, Institute of Information Engineering, Chinese Academy of Sciences, Beijing,, China, [zhouwei@iie.ac.cn](zhouwei@iie.ac.cn); Songlin Hu, Institute of Information Engineering, Chinese Academy of Sciences, Beijing,, China and University of Chin","cbCainsn6rKQWGRL","https://ap.wps.com/l/cbCainsn6rKQWGRL","pdf",1581993,3,1,35,"English","en",105,"# Introduction\n## LLMs as attackers in misinformation generation\n## LLMs as defenders in detection and verification pipelines\n## Role-layer framework for unified risks and defenses","[{\"question\":\"How do LLMs expand the risk of misinformation beyond generating false content?\",\"answer\":\"They enable attacks on social contexts, evidence sources, retrieval corpora, and verification workflows that defense systems rely on, increasing the threat surface for misinformation creation, amplification, contextualization, and concealment.\"},{\"question\":\"What is the role-layer framework introduced in the paper?\",\"answer\":\"It unifies misuse risks and defenses by describing LLMs along two dimensions: the role dimension (attacker, defender, or vulnerable verification component) and the layer dimension (content, social contexts, evidence environments, and verification workflows).\"},{\"question\":\"What open challenges does the paper identify for trustworthy real-world defense?\",\"answer\":\"It highlights the need for budgeted ecosystem-level risk evaluation instead of static detection accuracy, hardening LLM-centered verification pipelines against adversarial manipulation, and deploying auditable human-in-the-loop verification 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do LLMs expand the risk of misinformation beyond generating false content?","Question",{"text":75,"@type":76},"They enable attacks on social contexts, evidence sources, retrieval corpora, and verification workflows that defense systems rely on, increasing the threat surface for misinformation creation, amplification, contextualization, and concealment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the role-layer framework introduced in the paper?",{"text":80,"@type":76},"It unifies misuse risks and defenses by describing LLMs along two dimensions: the role dimension (attacker, defender, or vulnerable verification component) and the layer dimension (content, social contexts, evidence environments, and verification workflows).",{"name":82,"@type":73,"acceptedAnswer":83},"What open challenges does the paper identify for trustworthy real-world defense?",{"text":84,"@type":76},"It highlights the need for budgeted ecosystem-level risk evaluation instead of static detection accuracy, hardening LLM-centered verification pipelines against adversarial manipulation, and deploying auditable human-in-the-loop verification systems.","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 & 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