@inproceedings{liu-etal-2026-vlms,
title = "Do {VLM}s Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models",
author = "Liu, Zhining and
Wang, Tianyi and
Lin, Xiao and
Ouyang, Penghao and
Li, Gaotang and
Yang, Ze and
Liu, Hui and
Keswani, Sumit and
Pardeshi, Vishwa and
Zhao, Huijun and
Fan, Wei and
Tong, Hanghang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2079/",
pages = "41890--41909",
ISBN = "979-8-89176-395-1",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models
%A Liu, Zhining
%A Wang, Tianyi
%A Lin, Xiao
%A Ouyang, Penghao
%A Li, Gaotang
%A Yang, Ze
%A Liu, Hui
%A Keswani, Sumit
%A Pardeshi, Vishwa
%A Zhao, Huijun
%A Fan, Wei
%A Tong, Hanghang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-vlms
%X 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.
%U https://aclanthology.org/2026.findings-acl.2079/
%P 41890-41909
Markdown (Informal)
[Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models](https://aclanthology.org/2026.findings-acl.2079/) (Liu et al., Findings 2026)
ACL
- Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, and Hanghang Tong. 2026. Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41890–41909, San Diego, California, United States. Association for Computational Linguistics.