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

ACL ARR 2026 January Submission588 Authors

Keywords: misleading visualizations, chart understanding

Abstract: Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, enabling efficient data analysis and reporting but also introducing new misuse risks. In this work, we introduce ChartAttack, a novel framework for evaluating how MLLMs can be misused to generate misleading visualizations at scale. ChartAttack injects misleading techniques into chart designs, aiming to induce incorrect interpretations of the underlying data. Furthermore, we create AttackViz, a chart question-answering (QA) dataset where each (chart specification, QA) pair is labeled with effective misleaders and their induced incorrect answers. Experiments on AttackViz and on ChartQA show that ChartAttack significantly degrades the QA performance of MLLM readers, reducing accuracy by 19.6 points and 14.9 points, respectively. A human study further shows an average 20.2 point drop in accuracy for participants exposed to misleading charts generated by ChartAttack. Our findings highlight an urgent need for robustness and security considerations in the design, evaluation, and deployment of MLLM-based chart generation systems. We make our code and data publicly available.

Paper Type: Long

Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good

Research Area Keywords: misinformation detection and analysis

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 588