Vulnerability of LLMs to Vertically Aligned Text Manipulations

Zhecheng Li, Yiwei Wang, Bryan Hooi, Yujun Cai, Zhen Xiong, Nanyun Peng, and Kai-Wei Chang, in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL) , 2025 .

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Abstract

Vertical text input is commonly encountered in various real-world applications, such as mathematical computations and word-based Sudoku puzzles. While current large language models (LLMs) have excelled in natural language tasks, they remain vulnerable to variations in text formatting. Recent research demonstrates that modifying input formats, such as vertically aligning words for encoder-based models, can substantially lower accuracy in text classification tasks. While easily understood by humans, these inputs can significantly mislead models, posing a potential risk of bypassing detection in real-world scenarios involving harmful or sensitive information. With the expanding application of LLMs, a crucial question arises: Do decoder-based LLMs exhibit similar vulnerabilities to vertically formatted text input? In this paper, we investigate the impact of vertical text input on the performance of various LLMs across multiple text classification datasets and analyze the underlying causes. Our findings are as follows: (i) Vertical text input significantly degrades the accuracy of LLMs in text classification tasks. (ii) Chain of Thought (CoT) reasoning does not help LLMs recognize vertical input or mitigate its vulnerability, but few-shot learning with careful analysis does. (iii) We explore the underlying cause of the vulnerability by analyzing the inherent issues in tokenization and attention matrices.

Bib Entry

@inproceedings{li2025vulnerability,
  title = { Vulnerability of LLMs to Vertically Aligned Text Manipulations },
  author = {Li, Zhecheng and Wang, Yiwei and Hooi, Bryan and Cai, Yujun and Xiong, Zhen and Peng, Nanyun and Chang, Kai-Wei},
  year = { 2025 },
  booktitle = { Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL) }
}