# Researchers test MLLMs for generating and detecting fake multimodal social media posts

Digest AI · Research · published 2026-09-30T04:00:00Z

Canonical: https://digestai.news/story/researchers-test-mllms-for-generating-and-detecting-fake-multimodal-so

## Summary

A team of researchers explored whether multimodal large language models (MLLMs) can create and spot fake news on social media. They developed a multi-agent system with a story agent, an image agent, and a critic agent to produce over 9,000 paired fake posts across science, health, and entertainment topics. The framework aimed to generate realistic counterfeit content that mimics true news posts.

The study benchmarked 16 open- and closed-source MLLMs for both generating and detecting fake multimodal posts. Results showed most models struggled to match human-level accuracy, particularly in identifying image authenticity. The findings highlight gaps in current AI defenses against disinformation and provide a foundation for future research. Code and datasets are available on GitHub.

## Key points

- Researchers created a multi-agent system generating 9,000 fake multimodal social media posts in science, health, and entertainment
- Tested 16 MLLMs for detecting fake news, finding most models fall short of human-level accuracy
- Models performed poorly at identifying image authenticity in fake posts

## Why it matters

This research exposes vulnerabilities in AI’s ability to detect fake multimodal content, a critical gap as disinformation spreads across social media. It could drive better defenses and ethical AI development.

## Sources

1. [Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?](https://arxiv.org/abs/2609.35809) (arXiv cs.CL, 2026-09-30, primary source)

## Cite

Digest AI, "Researchers test MLLMs for generating and detecting fake multimodal social media posts", 30 September 2026, https://digestai.news/story/researchers-test-mllms-for-generating-and-detecting-fake-multimodal-so

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