aiqbench analysis
This research exposes critical vulnerabilities in multimodal large language models where malicious design choices in generated charts can systematically deceive both the AI and human interpreters into drawing incorrect conclusions. The study provides a robust framework for evaluating these "design misleaders" and demonstrates that specific fine-tuning strategies can significantly enhance model resilience against such adversarial prompting. These findings are essential for securing data visualization pipelines used in high-stakes decision-making environments.
ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation
arXiv:2601.12983v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving efficiency but introducing new misuse risks. We present ChartAttack, a framework for evaluating how MLLMs use design misleaders to generate charts that induce incorrect interpretations. We also introduce AttackViz, a chart question-answering (QA) dataset labeled with effective misleaders and their induced incorrect answers. ChartAttack reduces MLLM QA accuracy by 17.2 points in-domain and 11.9 points cross-domain. Conditional deception rates show targeted effects: correct answers shift to attacker-intended answers 11.2\% of the time in-domain and 11.7-14.9\% cross-domain, while originally incorrect answers rarely change. A controlled human study shows that ChartAttack-generated charts also reduce human QA performance. Finally, fine-tuning on AttackViz improves in-domain MLLM robustness to misleading charts. Our findings highlight the need for secure, robust MLLM chart generation. Code and data are publicly available on the project website.
Abstract:Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving efficiency but introducing new misuse risks. We present ChartAttack, a framework for evaluating how MLLMs use design misleaders to generate charts that induce incorrect interpretations. We also introduce AttackViz, a chart question-answering (QA) dataset labeled with effective misleaders and their induced incorrect answers. ChartAttack reduces MLLM QA accuracy by 17.2 points in-domain and 11.9 points cross-domain. Conditional deception rates show targeted effects: correct answers shift to attacker-intended answers 11.2\% of the time in-domain and 11.7-14.9\% cross-domain, while originally incorrect answers rarely change. A controlled human study shows that ChartAttack-generated charts also reduce human QA performance. Finally, fine-tuning on AttackViz improves in-domain MLLM robustness to misleading charts. Our findings highlight the need for secure, robust MLLM chart generation. Code and data are publicly available on the project website.
Camera-ready version accepted at EMNLP 2026 Main conference. Code and data are publicly available on the project website: this https URL
Computation and Language (cs.CL)
arXiv:2601.12983 [cs.CL]
(or arXiv:2601.12983v4 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2601.12983 Focus to learn more arXiv-issued DOI via DataCite
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