GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2602.24027v1
• Date: Fri, 27 Feb 2026 13:52:52 GMT
• Title: GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models
• Authors: Xingyu Zhu, Beier Zhu, Junfeng Fang, Shuo Wang, Yin Zhang, Xiang Wang, Xiangnan He,
• Abstract summary: GuardAlign is a training-free defense framework for large vision-language models.<n> OT-enhanced safety detection leverages optimal transport to measure distribution distances between image patches and unsafe semantics.<n>Cross-modal calibration strengthens the influence of safety prefixes by adaptively reallocating attention across layers.
• Score: 41.839145806211384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large vision-language models (LVLMs) have achieved remarkable progress in vision-language reasoning tasks, yet ensuring their safety remains a critical challenge. Recent input-side defenses detect unsafe images with CLIP and prepend safety prefixes to prompts, but they still suffer from inaccurate detection in complex scenes and unstable safety signals during decoding. To address these issues, we propose GuardAlign, a training-free defense framework that integrates two strategies. First, OT-enhanced safety detection leverages optimal transport to measure distribution distances between image patches and unsafe semantics, enabling accurate identification of malicious regions without additional computational cost. Second, cross-modal attentive calibration strengthens the influence of safety prefixes by adaptively reallocating attention across layers, ensuring that safety signals remain consistently activated throughout generation. Extensive evaluations on six representative MLLMs demonstrate that GuardAlign reduces unsafe response rates by up to 39% on SPA-VL, while preserving utility, achieving an improvement on VQAv2 from 78.51% to 79.21%.

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