@inproceedings{zhou-etal-2026-weak,
title = "Why does Weak-{OOD} Help? A Further Step Towards Understanding Jailbreaking {VLM}s",
author = "Zhou, Yuxuan and
Peng, Yuzhao and
Bai, Yang and
Gao, Kuofeng and
Zhang, Yihao and
Zhang, Yechao and
Chen, Xun and
Yu, Tao and
Dai, Tao and
Xia, Shu-Tao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1980/",
pages = "45038--45067",
ISBN = "979-8-89176-459-0",
abstract = "Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: researchers have developed various attack strategies that bypass the safety mechanisms of VLMs. Among these approaches, jailbreak methods based on the Out-of-Distribution (OOD) strategy have garnered widespread attention due to their simplicity and effectiveness. This paper further advances the understanding of OOD-based VLM jailbreak methods. We show that mild OOD manipulations can achieve stronger jailbreak performance than both clean inputs and overly strong perturbations, a non-monotonic pattern we define as ``weak-OOD''. We explain this phenomenon through a trade-off between two dominant factors: input intent perception and model refusal triggering. Our evidence suggests that these two factors respond differently to OOD manipulations, which is consistent with a discrepancy between broad pre-training robustness and narrower safety alignment. Building on this insight, we draw inspiration from optical character recognition (OCR) capability enhancement, a core task in the pre-training phase of mainstream VLMs. Leveraging this capability, we design JOCR (Jailbreak via OCR-Aware Embedded Text Perturbation), a simple VLM jailbreak method that achieves the best average ASR among evaluated baselines."
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<abstract>Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: researchers have developed various attack strategies that bypass the safety mechanisms of VLMs. Among these approaches, jailbreak methods based on the Out-of-Distribution (OOD) strategy have garnered widespread attention due to their simplicity and effectiveness. This paper further advances the understanding of OOD-based VLM jailbreak methods. We show that mild OOD manipulations can achieve stronger jailbreak performance than both clean inputs and overly strong perturbations, a non-monotonic pattern we define as “weak-OOD”. We explain this phenomenon through a trade-off between two dominant factors: input intent perception and model refusal triggering. Our evidence suggests that these two factors respond differently to OOD manipulations, which is consistent with a discrepancy between broad pre-training robustness and narrower safety alignment. Building on this insight, we draw inspiration from optical character recognition (OCR) capability enhancement, a core task in the pre-training phase of mainstream VLMs. Leveraging this capability, we design JOCR (Jailbreak via OCR-Aware Embedded Text Perturbation), a simple VLM jailbreak method that achieves the best average ASR among evaluated baselines.</abstract>
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%0 Conference Proceedings
%T Why does Weak-OOD Help? A Further Step Towards Understanding Jailbreaking VLMs
%A Zhou, Yuxuan
%A Peng, Yuzhao
%A Bai, Yang
%A Gao, Kuofeng
%A Zhang, Yihao
%A Zhang, Yechao
%A Chen, Xun
%A Yu, Tao
%A Dai, Tao
%A Xia, Shu-Tao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhou-etal-2026-weak
%X Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: researchers have developed various attack strategies that bypass the safety mechanisms of VLMs. Among these approaches, jailbreak methods based on the Out-of-Distribution (OOD) strategy have garnered widespread attention due to their simplicity and effectiveness. This paper further advances the understanding of OOD-based VLM jailbreak methods. We show that mild OOD manipulations can achieve stronger jailbreak performance than both clean inputs and overly strong perturbations, a non-monotonic pattern we define as “weak-OOD”. We explain this phenomenon through a trade-off between two dominant factors: input intent perception and model refusal triggering. Our evidence suggests that these two factors respond differently to OOD manipulations, which is consistent with a discrepancy between broad pre-training robustness and narrower safety alignment. Building on this insight, we draw inspiration from optical character recognition (OCR) capability enhancement, a core task in the pre-training phase of mainstream VLMs. Leveraging this capability, we design JOCR (Jailbreak via OCR-Aware Embedded Text Perturbation), a simple VLM jailbreak method that achieves the best average ASR among evaluated baselines.
%U https://aclanthology.org/2026.emnlp-main.1980/
%P 45038-45067
Markdown (Informal)
[Why does Weak-OOD Help? A Further Step Towards Understanding Jailbreaking VLMs](https://aclanthology.org/2026.emnlp-main.1980/) (Zhou et al., EMNLP 2026)
ACL
- Yuxuan Zhou, Yuzhao Peng, Yang Bai, Kuofeng Gao, Yihao Zhang, Yechao Zhang, Xun Chen, Tao Yu, Tao Dai, and Shu-Tao Xia. 2026. Why does Weak-OOD Help? A Further Step Towards Understanding Jailbreaking VLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 45038–45067, Budapest, Hungary. Association for Computational Linguistics.