ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation 事件

ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation arXiv:2601.12983v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving analysis and reporting efficiency while introducing new misuse risks. We present ChartAttack, a framework for evaluating how MLLMs can generate misleading charts at scale by injecting misleaders into chart designs to induce incorrect int