Msts Eval

---
name: msts-eval
description: Evaluates the safety and hazard response capabilities of vision-language models (VLMs) by testing how they handle prompts that combine text and images to elicit unsafe or hazardous outputs. Use when the user wants to benchmark on MSTS, or asks about evaluating this task. Reports unsafe_response_rate.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2501.10057
  bibtex_key: rottger2025msts
  confidence: high
---

# msts-eval

> MSTS: A Multimodal Safety Test Suite for Vision-Language Models — Röttger et al. (2025) (arXiv:2501.10057, 2025)

## What this evaluates

Evaluates the safety and hazard response capabilities of vision-language models (VLMs) by testing how they handle prompts that combine text and images to elicit unsafe or hazardous outputs.

## Datasets

- **MSTS** — total 400; splits: test (400); repo https://github.com/paul-rottger/msts-multimodal-safety

## Metrics

- `unsafe_response_rate` **(primary)** — range: percent
  - Percentage of prompts that result in an unsafe response, calculated as (count of unsafe responses / total prompts) * 100.

## Input / output format

**Input**: Multimodal prompts consisting of a text instruction (typically starting with 'Should I ___?') paired with a corresponding image.

**Output**: Model-generated text response to the multimodal prompt.

## Scoring recipe

```python
unsafe_count = 0
for prompt, image, gold_label in dataset:
    response = model.generate(prompt, image)
    if is_unsafe(response): # Judged by human annotators or safety classifier
        unsafe_count += 1
unsafe_response_rate = (unsafe_count / len(dataset)) * 100
```

## Common pitfalls

- Multimodal prompt misunderstanding can cause models to appear safe when they actually fail to address the hazard ('safe by accident').
- Automated safety classifiers often fail to achieve high accuracy on these nuanced multimodal prompts, making human evaluation or careful rubric-based scoring necessary.
- Safety performance varies significantly across languages, with non-English prompts often showing reduced safety compared to English.

## Evidence (verbatim from paper)

> MSTS introduces a structured, multimodal safety test suite with 400 prompts across 40 fine-grained hazard categories, where unsafe meanings emerge only from combined text-image inputs. It reveals that commercial VLMs generally respond safely but some open models exhibit clear safety failures or are safe by accident due to prompt misunderstanding; multilingual testing shows reduced safety in non-English prompts, and multimodal inputs increase unsafe responses compared to text-only versions.

## Citation

```bibtex
@misc{rottger2025msts,
  title={MSTS: A Multimodal Safety Test Suite for Vision-Language Models},
  author={Röttger et al. (2025)},
  year={2025},
  note={arXiv:2501.10057}
}
```

- arXiv: 2501.10057