Images Amplify Misinformation Sharing in Vision-Language Models

ArXi:2505.13302v2 Announce Type: replace As language and vision-language models (VLMs) become central to information access and online interaction, concerns grow about their potential to amplify misinformation. Human studies show that images boost the perceived credibility and shareability of information, raising the question of whether VLMs exhibit the same vulnerability. We present the first study examining how images influence VLMs' propensity to reshare news content, how this effect varies across model families, and how persona conditioning and content attributes modulate such behavior.