Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911440815783936 |
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| author | Loth, Alexander Kappes, Martin Pahl, Marc-Oliver |
| author_facet | Loth, Alexander Kappes, Martin Pahl, Marc-Oliver |
| contents | Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and multimodal systems. Central to this work are our practical contributions: JudgeGPT, a platform for evaluating human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine for research. Together, these tools form an experimental pipeline for studying how humans perceive and detect AI-generated misinformation. Our findings show that detection capabilities have improved, but the competition between generation and detection continues. We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI. This work contributes to research addressing the adverse impacts of AI on information quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21963 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems Loth, Alexander Kappes, Martin Pahl, Marc-Oliver Computers and Society Artificial Intelligence Computation and Language Social and Information Networks I.2.7; K.4.1; K.4.2; H.3.5 Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and multimodal systems. Central to this work are our practical contributions: JudgeGPT, a platform for evaluating human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine for research. Together, these tools form an experimental pipeline for studying how humans perceive and detect AI-generated misinformation. Our findings show that detection capabilities have improved, but the competition between generation and detection continues. We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI. This work contributes to research addressing the adverse impacts of AI on information quality. |
| title | Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems |
| topic | Computers and Society Artificial Intelligence Computation and Language Social and Information Networks I.2.7; K.4.1; K.4.2; H.3.5 |
| url | https://arxiv.org/abs/2601.21963 |