ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation
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 answers. ChartAttack significantly degrades QA performance, reducing MLLM accuracy by 17.2 points in-domain and 11.9 cross-domain. A controlled human study shows that misleading charts generated by ChartAttack reduce human chart QA performance. Finally, we demonstrate that AttackViz can be used to fine-tune MLLMs to improve robustness against misleading charts. 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Efficient but Vulnerable: Benchmarking and Defending LLM Batch Prompting Attack
Batch prompting, which combines a batch of multiple queries sharing the same context in one inference, has emerged as a promising solution to reduce inference costs. However, our study reveals a significant security vuln…
8kBenchmarkingLogical ReasoningChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates
Large language models (LLMs) are expected to follow instructions from users and engage in conversations. Techniques to enhance LLMs' instruction-following capabilities typically fine-tune them using data structured accor…
Instruction FollowingSafety AlignmentEnhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting
Numerous works are proposed to align large language models (LLMs) with human intents to better fulfill instructions, ensuring they are trustful and helpful. Nevertheless, some human instructions are often malicious or mi…
counterfactualFact CheckingSecurity Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
Software security vulnerabilities allow attackers to perform malicious activities to disrupt software operations. Recent Transformer-based language models have significantly advanced vulnerability detection, surpassing t…
Graph Neural NetworkVulnerability DetectionCan LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection
Despite their remarkable success, large language models (LLMs) have shown limited ability on applied tasks such as vulnerability detection. We investigate various prompting strategies for vulnerability detection and, as …
Vulnerability Detection