发现视觉语言模型的道德判断极易被简单干扰误导，稳定性差。
Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models
- 用多种无意义的文本/图像扰动测试模型道德判断
- 多数模型在轻微干扰下道德立场频繁翻转
- 强指令跟随模型更易被说服，适合安全评估者关注
尽管在提升视觉语言模型（VLMs）道德对齐方面已有大量努力，但其在真实场景中伦理判断的稳定性仍不明确。本文研究了VLMs的道德鲁棒性，即在不改变底层道德情境的前提下，维持道德判断的能力。我们系统地使用一系列模型无关的多模态扰动对VLMs进行探测，发现其道德立场极为脆弱，常在简单操作下发生反转。分析显示，这种脆弱性在不同扰动类型、道德领域和模型规模间普遍存在，包括一种‘迎合权宜’的权衡现象：更强的指令遵循模型更易受说服。我们进一步表明，轻量级推理时干预可部分恢复道德稳定性。结果表明，仅实现道德对齐不足以为继，道德鲁棒性是负责任部署VLMs的必要条件。
原文摘要 · Abstract (English)
Despite substantial efforts toward improving the moral alignment of Vision-Language Models (VLMs), it remains unclear whether their ethical judgments are stable in realistic settings. This work studies moral robustness in VLMs, defined as the ability to preserve moral judgments under textual and visual perturbations that do not alter the underlying moral context. We systematically probe VLMs with a diverse set of model-agnostic multimodal perturbations and find that their moral stances are highly fragile, frequently flipping under simple manipulations. Our analysis reveals systematic vulnerabilities across perturbation types, moral domains, and model scales, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion. We further show that lightweight inference-time interventions can partially restore moral stability. These results demonstrate that moral alignment alone is insufficient and that moral robustness is a necessary criterion for the responsible deployment of VLMs.
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