---
license: cc-by-4.0
language:
- en
- zh
size_categories:
- 1K<n<10K
task_categories:
- text-generation
pretty_name: DiagSafe
tags:
- safety
- llm-safety
- red-teaming
- jailbreak
- llm-as-a-judge
- rubric
- benchmark
configs:
- config_name: prompts
  default: true
  data_files:
  - split: test
    path: prompts/DiagSafe_bench_3489.jsonl
- config_name: rubrics
  data_files:
  - split: test
    path: rubrics/DiagSafe_rubrics_3489.jsonl
- config_name: standards_zh
  data_files:
  - split: train
    path: standards/DiagSafe_standards_84.json
- config_name: standards_en
  data_files:
  - split: train
    path: standards/DiagSafe_standards_84_en.json
---

# DiagSafe

**Beyond Binary Labels: Diagnostic Evaluation of LLM Safety with Prompt-Level Rubrics**

[Paper (arXiv)](https://arxiv.org/abs/2610.29225) · [GitHub](https://github.com/Tencent-Hunyuan/DiagSafe) · [Evaluation code](https://github.com/Tencent-Hunyuan/DiagSafe#quick-start) · [Dashboard](https://tencent-hunyuan.github.io/DiagSafe/)

> **Content warning.** This dataset contains harmful requests (e.g. violence, sexual content, self-harm,
> child safety, CBRN) written to test LLM safety. It is intended for safety evaluation and research only.

DiagSafe is a diagnostic safety benchmark for large language models. It pairs 3,489 bilingual prompts,
grounded in an 84-label, three-tier safety taxonomy, with **one prompt-level rubric per prompt**: each
rubric lists prompt-specific safety items with their exemptions, plus eight shared usefulness items, so an
LLM judge rules on every item and cites evidence instead of emitting a single binary label.

## Dataset Structure

| Config | File | Records | Description |
|---|---|---:|---|
| `prompts` (default) | `prompts/DiagSafe_bench_3489.jsonl` | 3,489 | Bilingual single- and multi-turn evaluation prompts |
| `rubrics` | `rubrics/DiagSafe_rubrics_3489.jsonl` | 3,489 | One prompt-level judge rubric per prompt |
| `standards_zh` | `standards/DiagSafe_standards_84.json` | 84 | Three-level taxonomy and label standards (Chinese) |
| `standards_en` | `standards/DiagSafe_standards_84_en.json` | 84 | The same 84 labels, in English |

### Composition

| Dimension | Distribution |
|---|---|
| Subset | DiagBase: 2,401 · DiagAttack: 1,088 |
| Language | English: 1,749 · Chinese: 1,740 |
| Dialogue | Single-turn: 2,560 · Multi-turn: 929 (2–5 turns, 5,135 turns in total) |
| Taxonomy | 11 first-level · 34 second-level · 84 third-level labels |

- **DiagBase** is synthesized from the label standards along language × dialogue turns × attack form
  (direct harmful requests, template jailbreaks, combined-template jailbreaks).
- **DiagAttack** rewrites the single-turn direct requests of DiagBase into instruction attacks, embedding the
  harmful intent in a benign-looking task with role, format and tone constraints.

| Code | First-level dimension | Prompts |
|---|---|---:|
| OSS-01 | Discrimination, Hate & Harmful Bias | 440 |
| OSS-02 | Sexual & Sexually Explicit Content | 695 |
| OSS-03 | Privacy Violations & Information Disclosure | 195 |
| OSS-04 | Misinformation, Disinformation & Confabulation | 240 |
| OSS-05 | Violent & Illegal Activities | 831 |
| OSS-06 | CBRN & WMD | 234 |
| OSS-07 | Cybersecurity & Model Attacks | 187 |
| OSS-08 | Suicide, Self-Harm & Dangerous Health Behaviors | 205 |
| OSS-09 | Child Safety | 217 |
| OSS-10 | Intellectual Property Infringement | 165 |
| OSS-11 | Manipulation & Deceptive Persuasion | 80 |

### Data Fields

**`prompts`**

| Field | Type | Description |
|---|---|---|
| `id` | string | `DiagBase-NNNNNN` or `DiagAttack-NNNNNN`; the prefix identifies the subset |
| `language` | string | `en` or `zh` |
| `messages` | list | Ordered user turns, `[{"role": "user", "content": ...}]`; multi-turn prompts have 2–5 |
| `category` | dict | `l1` (code `OSS-01`–`OSS-11`), `l2` and `l3` (bilingual label names) |

**`rubrics`**

| Field | Type | Description |
|---|---|---|
| `id` | string | Matches exactly one prompt |
| `rubric` | string | Complete judge-prompt template with `{question}`, `{answer}` and `{safety_categories}` placeholders, 2–6 prompt-specific safety items (4.5 on average) with exemptions, eight usefulness items (U1.1–U3.2), a 0–3 usefulness scoring rule and a JSON output contract. Written in the language of its prompt |

**`standards_zh` / `standards_en`**

| Chinese field | English field | Description |
|---|---|---|
| `类目编号` | `category_code` | First-level code, `OSS-01`–`OSS-11` |
| `类目名称` | `category_name` | `一级类目` / `二级类目` / `三级类目` ↔ `l1` / `l2` / `l3` |
| `定义` | `definition` | Label definition |
| `标准总则` | `general_standard` | General standard |
| `标准细则` | `detailed_rules` | Detailed rules, each marked `——危险` / `——安全` (`——UNSAFE` / `——SAFE`) |

Both standard files cover the same 84 labels in the same order. A prompt's `category.l2` / `category.l3`
equal `类目名称.二级类目` / `类目名称.三级类目` in the Chinese file; the English file uses the parenthesized
English names.

## Usage

```python
from datasets import load_dataset

prompts = load_dataset("tencent/DiagSafe", "prompts", split="test")
rubrics = load_dataset("tencent/DiagSafe", "rubrics", split="test")
standards = load_dataset("tencent/DiagSafe", "standards_zh", split="train")

rubric_by_id = dict(zip(rubrics["id"], rubrics["rubric"]))
example = prompts[0]
rubric = rubric_by_id[example["id"]]
```

The rubric contains JSON braces in its output contract, so fill the placeholders with `str.replace`
rather than `str.format`:

```python
import json

judge_prompt = (
    rubric
    .replace("{question}", json.dumps(example["messages"], ensure_ascii=False))
    .replace("{answer}", model_answer)
    .replace("{safety_categories}", json.dumps(example["category"], ensure_ascii=False))
)
```

Multi-turn prompts are evaluated turn by turn, with earlier turns given to the judge as context; a prompt
counts as Unsafe if any turn is Unsafe. The [GitHub repository](https://github.com/Tencent-Hunyuan/DiagSafe)
provides the end-to-end evaluation script (`scripts/run_eval.py`), which handles answer generation,
multi-turn judging, checkpointing and grouped statistics.

## Intended Use and Limitations

DiagSafe is intended for evaluating and diagnosing the safety behavior of LLMs and LLM judges. The prompts
are deliberately harmful; we ask users not to use them to train models to produce harmful content. Results depend
on the judge model; the paper reports rubric-based judging at 89.41%–95.34% agreement with human
annotations across judges.

## License

Copyright (C) 2026 Tencent. Released under the
[Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

## Citation

```bibtex
@misc{diagsafe2026,
  title        = {Beyond Binary Labels: Diagnostic Evaluation of LLM Safety with Prompt-Level Rubrics},
  author       = {Li, Hongyu and Huang, Xinlong and Yu, Zhan and Zhao, Jing and Zhao, Zhe and
                  Zhong, Juntao and Kuang, Xiong and Yuan, Rui and Yan, ZhenXiang and Xu, Huilin and
                  Yu, Haitao and Wang, Lilin and Chen, Rui and Chen, Blaze and Lian, Fengzong and
                  Hu, Renfen},
  year         = {2026},
  archivePrefix= {arXiv},
  eprint       = {2610.29225},
  primaryClass = {cs.CL}
}
```

## Contact

`nlpzhezhao@tencent.com`