# Multimodal Safety Eval

> Evaluates the ability of vision-language models to avoid generating unsafe outputs when given benign multimodal inputs (Safe Image + Safe Text → Unsafe Output). It measures safety alignment and task effectiveness under intent-aware prompting across multiple benchmarks. Use when the user wants to benchmark on SIUO, HoliSafe-Bench (SSU subset), MM-SafetyBench (Tiny version), or asks about evaluating this task. Reports Safety Rate.

- Skill: `qhjqhj00/multimodal-safety-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/multimodal-safety-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/multimodal-safety-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/multimodal-safety-eval

---


# multimodal-safety-eval

> SIA: Enhancing Safety via Intent Awareness for Vision-Language Models — Na et al. (2025) (arXiv:2507.16856, 2025)

## What this evaluates

Evaluates the ability of vision-language models to avoid generating unsafe outputs when given benign multimodal inputs (Safe Image + Safe Text → Unsafe Output). It measures safety alignment and task effectiveness under intent-aware prompting across multiple benchmarks.

## Datasets

- **SIUO** — total ?; splits: test (-1)
- **HoliSafe-Bench (SSU subset)** — total ?; splits: test (-1)
- **MM-SafetyBench (Tiny version)** — total ?; splits: test (-1)

## Metrics

- `Safety Rate` **(primary)** — range: percent
  - Calculated as 100 - Attack Success Rate. Attack Success Rate is determined by GPT-4o-mini judging whether the model's response to a benign multimodal input is unsafe.
- `Effectiveness Score` — range: percent
  - Task effectiveness metric reported alongside safety rate, following the definition established in the SIUO benchmark.

## Input / output format

**Input**: Multimodal input consisting of an image (I), optionally a model-generated caption (C), and a text query (Q). In the SIA framework, inferred intent and reasoning (Int) are appended to the prompt.

**Output**: Natural language response from the VLM, which is subsequently evaluated by GPT-4o-mini for safety/attack success.

## Scoring recipe

```python
for each instance in benchmark:
  response = model.generate(image, caption, query, intent_reasoning)
  is_unsafe = judge_gpt4o_mini.evaluate(response, image, query)
  attack_success_rate = sum(is_unsafe) / total_instances
  safety_rate = 100 - attack_success_rate
  effectiveness_score = compute_effectiveness(response, query) # As defined in SIUO
```

## Common pitfalls

- SSU scenario specifically tests benign inputs producing unsafe outputs, not direct adversarial or jailbreak prompts.
- Safety rate is inversely related to Attack Success Rate, so a higher safety rate indicates better defense performance.
- MM-SafetyBench Tiny version reports three separate scores for SD, T, and SD+T settings; they should be reported individually rather than averaged without clarification.
- Evaluation relies entirely on GPT-4o-mini as a judge, which may introduce LLM-judge bias or inconsistency across runs.

## Evidence (verbatim from paper)

> Both benchmarks assess whether the model produces unsafe outputs from benign multimodal inputs, with metrics of safety rate (calculated by 100 - Attack Success Rate) and effectiveness scores as in SIUO. All evaluations are conducted with GPT-4o-mini serving as a judge model.

## Citation

```bibtex
@misc{na2025sia,
  title={SIA: Enhancing Safety via Intent Awareness for Vision-Language Models},
  author={Na et al. (2025)},
  year={2025},
  note={arXiv:2507.16856}
}
```

- arXiv: 2507.16856