StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs

Yang Luo, Liu Xinran, TianTian Ji, Zhiyi Yin, Lingyun Peng, Shuyu Li


Abstract
Multimodal Large Language Models (MLLMs) excel at structural reasoning yet suffer from a sharp logical brittleness in structural consistency. We term this phenomenon Structural Cognitive Overload (SCO), a byproduct of the contention between deep reasoning and safety alignment. However, prior work has predominantly targeted typographic and pixel-level perturbations, leaving the study of SCO largely unexplored. To this end, we propose StructBreak, an automated end-to-end framework designed to quantify SCO. By leveraging StructBreak, we uncover a novel higher-order cognitive overload attack paradigm; notably, this attack operates under a practical black-box setting, requiring no internal model access. Consequently, we utilize this framework to establish a comprehensive benchmark spanning ten diverse threat scenarios. Empirical evaluations on six leading MLLMs reveal that SCO readily triggers toxic generation, yielding a 92% average ASR (up to 97% on Gemini 2.5). To elucidate the mechanism of SCO, we further conduct model-level interpretations spanning attention dynamics, latent space topology, and geometric analysis. Our findings reveal that StructBreak acts as a novel structural channel to circumvent safety filters. Furthermore, the limited efficacy of inherent safety mechanisms underscores that current alignment paradigms are insufficient for the era of complex multimodal reasoning.
Anthology ID:
2026.findings-acl.293
Volume:
Findings of the Association for Computational Linguistics: ACL 2026
Month:
July
Year:
2026
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San Diego, California, United States
Editors:
Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
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Findings
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Publisher:
Association for Computational Linguistics
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Pages:
5901–5923
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https://preview.aclanthology.org/ingest-acl/2026.findings-acl.293/
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Cite (ACL):
Yang Luo, Liu Xinran, TianTian Ji, Zhiyi Yin, Lingyun Peng, and Shuyu Li. 2026. StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5901–5923, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):
StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs (Luo et al., Findings 2026)
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