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arXiv · 2603.08486

Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images

Qishun Yang·Shu Yang·Lijie Hu·Di Wang
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Abstract

Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods require explicit safety labels or contrastive data; yet, threat-related concepts are concrete and visually depictable, while safety concepts, like helpfulness, are abstract and lack visual referents. Inspired by the Self-Fulfilling mechanism underlying emergent misalignment, we propose Visual Self-Fulfilling Alignment (VSFA). VSFA fine-tunes vision-language models (VLMs) on neutral VQA tasks constructed around threat-related images, without any safety labels. Through repeated exposure to threat-related visual content, models internalize the implicit semantics of vigilance and caution, shaping safety-oriented personas. Experiments across multiple VLMs and safety benchmarks demonstrate that VSFA reduces the attack success rate, improves response quality, and mitigates over-refusal while preserving general capabilities. Our work extends the self-fulfilling mechanism from text to visual modalities, offering a label-free approach to VLMs alignment.

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Qishun Yang, Shu Yang, Lijie Hu, Di Wang. 2026-03-09. Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images. https://arxiv.org/abs/2603.08486

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