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 interpretations. We also introduce AttackViz, a chart question-answering (QA) dataset where each (chart specification, QA) pair is labeled with effective misleaders and their induced incorrect a
The rapid deployment and integration of MLLMs into critical business intelligence and analytical workflows necessitate a robust understanding of their vulnerabilities.
This research highlights a significant new vector for disinformation and manipulation, directly impacting decision-making reliance on AI-generated insights.
The perceived infallibility of MLLM-generated charts for data analysis is challenged, requiring new validation and security protocols.
- · AI Red Teams
- · Cybersecurity startups
- · Data validation platforms
- · Unsecured MLLM deployments
- · Organizations relying solely on AI for chart analysis
- · Malicious actors without sophisticated obfuscation techniques
Increased focus on adversarial machine learning research specifically targeting MLLMs for data visualization.
Development and adoption of explainable AI (XAI) tools to scrutinize the generation process of charts and identify potential misleaders.
New regulatory frameworks and compliance standards for AI-generated analytical outputs, particularly in sensitive sectors like finance and intelligence.
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