MSTS: A Multimodal Safety Test Suite for Vision-Language Models
Vision-language models (VLMs), which process image and text inputs, are
increasingly integrated into chat assistants and other consumer AI
applications. Without proper safeguards, however, VLMs may give harmful advice
(e.g. how to self-harm) or encourage unsafe behaviours (e.g. to consume drugs).
Despite these clear hazards, little work so far has evaluated VLM safety and
the novel risks created by multimodal inputs. To address this gap, we introduce
MSTS, a Multimodal Safety Test Suite for VLMs. MSTS comprises 400 test prompts
across 40 fine-grained hazard categories. Each test prompt consists of a text
and an image that only in combination reveal their full unsafe meaning. With
MSTS, we find clear safety issues in several open VLMs. We also find some VLMs
to be safe by accident, meaning that they are safe because they fail to
understand even simple test prompts. We translate MSTS into ten languages,
showing non-English prompts to increase the rate of unsafe model responses. We
also show models to be safer when tested with text only rather than multimodal
prompts. Finally, we explore the automation of VLM safety assessments, finding
even the best safety classifiers to be lacking.
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Giuseppe Attanasio
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Janis Goldzycher
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Alicia Parrish
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Rishabh Bhardwaj
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Roman Eng
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Donya Rooein
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Patrick Schramowski
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Xudong Shen
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Richard Willats
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Andrea Zugarini
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Bertie Vidgen
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
Vision-language models (VLMs), which process image and text inputs, are
increasingly integrated into chat assistants and other consumer AI
applications. Without proper safeguards, however, VLMs may give harmful advice
(e.g. how to self-harm) or encourage unsafe behaviours (e.g. to consume drugs).
Despite these clear hazards, little work so far has evaluated VLM safety and
the novel risks created by multimodal inputs. To address this gap, we introduce
MSTS, a Multimodal Safety Test Suite for VLMs. MSTS comprises 400 test prompts
across 40 fine-grained hazard categories. Each test prompt consists of a text
and an image that only in combination reveal their full unsafe meaning. With
MSTS, we find clear safety issues in several open VLMs. We also find some VLMs
to be safe by accident, meaning that they are safe because they fail to
understand even simple test prompts. We translate MSTS into ten languages,
showing non-English prompts to increase the rate of unsafe model responses. We
also show models to be safer when tested with text only rather than multimodal
prompts. Finally, we explore the automation of VLM safety assessments, finding
even the best safety classifiers to be lacking.
MSTS, a Multimodal Safety Test Suite, identifies safety issues in VLMs by evaluating responses to multimodal prompts across various hazard categories.
Vision-language models VLMs Multimodal Safety Test Suite MSTS hazard categories multimodal inputs safety assessments safety classifiers