Coordinated Deception: Generative Agents for Multi-Agent Fake News Generation

Abstract

The proliferation of Large Language Models (LLMs) has significantly lowered the barriers to generating sophisticated misinformation, posing unprecedented challenges to fake news detection systems. Although generative agents have shown promise in constructive applications like fact-checking workflows, their potential for collaborative fake news generation remains unexplored. This paper introduces the first multi-agent framework for fake news generation, where specialized LLM-powered agents collaboratively transform authentic news articles into falsified yet plausible versions. Our pipeline employs five specialized agents: a Semantic Analyzer that extracts information-dense components, a Salient Sentence Editor and Number Modifier that manipulate key textual and numerical elements, a Narrative Modifier that ensures overall coherence, and a Title Editor that generates contextually appropriate headlines. We evaluate this system on two benchmark datasets, the ISOT Fake News Dataset and the All The News Dataset, against six state-of-the-art detection models. Our results demonstrate that the proposed framework achieves deception success rates exceeding 90% while maintaining high semantic similarity to original articles. Notably, even smaller generative models can surpass 80% success against high-capacity detectors, highlighting critical vulnerabilities in current fake news detection systems and underscoring the urgent need for more robust defense mechanisms against coordinated generative AI attacks.