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

TL;DR

This paper examines the evolving threat of AI-generated misinformation, introduces practical tools for research, and discusses mitigation strategies to address the impact of LLMs on information integrity.

Contribution

It presents JudgeGPT and RogueGPT as new tools for studying human perception of AI misinformation and offers updated insights into detection and mitigation methods.

Findings

Detection of AI misinformation has improved.

The arms race between generation and detection continues.

Mitigation strategies include LLM-based detection and inoculation.

Abstract

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.