---
license: other
language:
- en
tags:
- synthetic
- jailbreak
- refusal
- safety
---
# LLM Refusal Training Dataset

A large-scale dataset designed to teach LLMs how to safely refuse jailbreak attempts, prompt injections, and policy-violating requests.

## Dataset Description

This dataset contains **30GB** of `(category, prompt, response)` triplets pairing simulated adversarial prompts with safe, helpful refusals. The data is non-operational and does not contain real exploits or harmful instructions.

### Columns

| Column     | Type   | Description                                                                 |
|------------|--------|-----------------------------------------------------------------------------|
| `category` | string | Attack taxonomy (e.g., `prompt_injection`, `roleplay_bypass`, `authority_claim`) |
| `prompt`   | string | Simulated adversarial user message                                          |
| `response` | string | Safe refusal with optional helpful redirection                              |

### Categories Covered

- Prompt injection & direct override
- Roleplay / persona bypass
- Authority & developer-mode claims
- Encoding & obfuscation tricks
- Hypothetical & fictional framing
- Incremental escalation & multi-turn social engineering
- Policy override & emotional pressure
- Tool abuse & data exfiltration attempts

## Usage

```python
from datasets import load_dataset

ds = load_dataset(
    "YOUR_USERNAME/llm-refusal-training-30gb",
    data_files="data/llm_refusal_training.csv.gz",
    split="train"
)
```

### Intended Use
1. Supervised fine-tuning for refusal behavior
2. Safety alignment research
3. Red-team evaluation benchmarks

### Limitations

1. May not capture all real-world attack distributions
2. Refusals follow a limited set of patterns; consider diversifying for production use
3. Not a substitute for human-curated safety data

### License
Open Data Attribution Training Disclosure License (ODATL‑1.0)