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        <title>Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment</title>
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    <abstract>Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we identify a critical vulnerability inherent to this paradigm: lowering image resolution inadvertently catalyzes jailbreaking. Our experiments reveal that the safety defenses of SOTA models deteriorate sharply as resolution degrades, surprisingly persisting even when text remains legible. We attribute this to “Cognitive Overload“, hypothesizing that the effort required to decipher degraded inputs diverts attentional resources from safety auditing. This phenomenon is consistent across various visual perturbations, including noise and geometric distortion. To address this, we propose a simple “Structured Cognitive Offloading” strategy that mitigates these risks by enforcing a serialized pipeline to decouple visual transcription from safety assessment. Our work exposes a significant risk in vision-based compression and provides critical insights for the secure design of future MLLMs.</abstract>
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%0 Conference Proceedings
%T Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment
%A Song, Zhixue
%A Han, Boyan
%A Wang, Yiwei
%A Zhang, Chi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F song-etal-2026-hard
%X Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we identify a critical vulnerability inherent to this paradigm: lowering image resolution inadvertently catalyzes jailbreaking. Our experiments reveal that the safety defenses of SOTA models deteriorate sharply as resolution degrades, surprisingly persisting even when text remains legible. We attribute this to “Cognitive Overload“, hypothesizing that the effort required to decipher degraded inputs diverts attentional resources from safety auditing. This phenomenon is consistent across various visual perturbations, including noise and geometric distortion. To address this, we propose a simple “Structured Cognitive Offloading” strategy that mitigates these risks by enforcing a serialized pipeline to decouple visual transcription from safety assessment. Our work exposes a significant risk in vision-based compression and provides critical insights for the secure design of future MLLMs.
%U https://aclanthology.org/2026.findings-acl.983/
%P 19643-19658

Markdown (Informal)

[Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment](https://aclanthology.org/2026.findings-acl.983/) (Song et al., Findings 2026)

• Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment (Song et al., Findings 2026)

ACL

• Zhixue Song, Boyan Han, Yiwei Wang, and Chi Zhang. 2026. Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19643–19658, San Diego, California, United States. Association for Computational Linguistics.