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
- Address:
- San Diego, California, United States
- Editors:
- Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5901–5923
- Language:
- URL:
- https://preview.aclanthology.org/ingest-acl/2026.findings-acl.293/
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl/2026.findings-acl.293.pdf