# Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems

arXiv: https://arxiv.org/abs/2601.21963 · HTML: https://mlsanity.com/paper/2601.21963/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 63 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://mlsanity.com/paper/2402.17764/) (2024)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models](https://mlsanity.com/paper/2310.00737/) (2023)
- [Combating Disinformation in a Social Media Age](https://mlsanity.com/paper/2007.07388/) (2020)
- [From Deception to Detection: The Dual Roles of Large Language Models in Fake News](https://mlsanity.com/paper/2409.17416/) (2024)
- [Understanding Knowledge Drift in LLMs through Misinformation](https://mlsanity.com/paper/2409.07085/) (2024)
- [Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks](https://mlsanity.com/paper/2601.15277/) (2026)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth](https://mlsanity.com/paper/2601.00306/) (2026)
- [Simulating Misinformation Propagation in Social Networks using Large Language Models](https://mlsanity.com/paper/2511.10384/) (2025)
- [Understanding Reader Perception Shifts upon Disclosure of AI Authorship](https://mlsanity.com/paper/2510.24011/) (2025)
- [Toward Generalized Detection of Synthetic Media: Limitations, Challenges, and the Path to Multimodal Solutions](https://mlsanity.com/paper/2511.11116/) (2025)
- [Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection](https://mlsanity.com/paper/2512.21039/) (2025)
- [MultiCaption: Detecting disinformation using multilingual visual claims](https://mlsanity.com/paper/2601.11220/) (2026)
- [An Agentic Operationalization of DISARM for FIMI Investigation on Social Media](https://mlsanity.com/paper/2601.15109/) (2026)

## Built on by (most cited first, 7 of 7)

- [Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild](https://mlsanity.com/paper/2601.22871/) (2026)
- [Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection](https://mlsanity.com/paper/2602.03423/) (2026)
- [The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance](https://mlsanity.com/paper/2602.02100/) (2026)
- [Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News](https://mlsanity.com/paper/2604.03755/) (2026)
- [ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies](https://mlsanity.com/paper/2605.27045/) (2026)
- [Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens](https://mlsanity.com/paper/2608.21389/) (2026)
- [The Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning](https://mlsanity.com/paper/2609.29045/) (2026)

## Abstract (arXiv)

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.