---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
- security
- cyber
- nuclei
pretty_name: CVE to Nuclei Template
---
# Ernest Nuclei Templates Dataset v3

A comprehensive dataset for training models to generate Nuclei security scanning templates from vulnerability descriptions.

## Dataset Description

This dataset contains 11,590 training examples for generating Nuclei YAML templates in JSON IR (Intermediate Representation) format from structured vulnerability specifications.

### Format

Each training example consists of:

1. **`id`**: Unique identifier (CVE ID, CWE ID, or template name)
2. **`prompt`**: Structured input with 4 sections (see below)
3. **`completion`**: JSON IR output matching `nuclei_ir.schema.json`

### Prompt Structure

The prompt contains exactly 4 sections in this order:

#### **[CANONICAL_RECORD]**
Structured vulnerability representation as JSON:
```json
{
  "cve_id": "cve-2024-1234",
  "summary": "Description of vulnerability",
  "affected_products": [{"vendor": "...", "product": "...", "version_range": "..."}],
  "paths": ["/admin/upload.php"],
  "indicators": ["error", "exception"],
  "severity_hint": "high"
}
```

#### **[RETRIEVED_TEMPLATES]**
JSON array of 2-3 similar templates (flattened, structural):
```json
[
  {
    "id": "cve-2024-5678",
    "severity": "high",
    "tags": ["cve", "sqli", "apache"],
    "requests": [
      {
        "method": "GET",
        "paths": ["{{BaseURL}}/admin.php?id=1"],
        "matcher_types": ["word", "status"]
      }
    ]
  }
]
```

#### **[POC_SNIPPETS]**
JSON array of PoC snippets (or empty array):
```json
[
  {
    "source": "nvd",
    "url": "https://...",
    "snippet": "curl -X POST ..."
  }
]
```

#### **[INSTRUCTION]**
Fixed instruction text:
```
Generate a valid Nuclei template as JSON IR. Output ONLY the JSON object matching nuclei_ir.schema.json. No markdown, no explanations, no code fences.
```

### Completion Format

Pure JSON IR matching `nuclei_ir.schema.json`:
```json
{
  "id": "cve-2024-1234",
  "info": {
    "name": "XSS Vulnerability in Product X",
    "author": "ernest-generator",
    "severity": "high",
    "description": "...",
    "tags": ["cve", "xss"],
    "reference": ["https://..."]
  },
  "requests": [
    {
      "method": "GET",
      "path": ["{{BaseURL}}/vulnerable.php"],
      "matchers": [...]
    }
  ],
  "safety_flags": {...},
  "limits": {...},
  "metadata": {...}
}
```

**No markdown, no code fences, no extra text** - ONLY the JSON object.

## Dataset Statistics

- **Total examples**: 11,590
- **Train split**: 8,112 (70%)
- **Test split**: 1,736 (15%)
- **Validation split**: 1,742 (15%)

### By Category

- **CVE-based**: 3,666 (31.6%) - Enriched with NVD CVE data
- **CWE-based**: 2,499 (21.6%) - Enriched with MITRE CWE data
- **Generic**: 5,425 (46.8%) - Self-contained templates

### File Sizes

- `full.jsonl`: 60 MB
- `train.jsonl`: 42 MB
- `test.jsonl`: 9 MB
- `validation.jsonl`: 9 MB

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load from local files
dataset = load_dataset('json', data_files={
    'train': 'train.jsonl',
    'test': 'test.jsonl',
    'validation': 'validation.jsonl'
})

# Or from Hugging Face Hub
# dataset = load_dataset('OzLabs/ernest-nuclei-templates-v3')

# Access a training example
example = dataset['train'][0]
print(f"ID: {example['id']}")
print(f"\nPrompt:\n{example['prompt'][:500]}...")
print(f"\nCompletion:\n{example['completion'][:500]}...")
```

### Fine-tuning Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Qwen/Qwen2.5-Coder-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def format_example(example):
    """Format example for instruction tuning."""
    return {
        "input": example["prompt"],
        "output": example["completion"]
    }

# Prepare dataset
formatted_dataset = dataset.map(format_example)

# Fine-tune with your preferred framework (TRL, Axolotl, etc.)
```

## Data Source

Templates extracted from [ProjectDiscovery/nuclei-templates](https://github.com/projectdiscovery/nuclei-templates) repository and enriched with:

- **CVE Data**: National Vulnerability Database (NVD) API 2.0
- **CWE Data**: MITRE Common Weakness Enumeration
- **Template Similarity**: Pre-computed similar templates for few-shot learning

## Schema Validation

~70% of examples pass strict JSON schema validation. Validation failures are mostly due to:
- Empty path arrays (legitimate for SSL/TLS templates)
- Templates with minimal matchers

The prompt structure (4 sections) is valid for 100% of examples.

## Intended Use

This dataset is designed for:

1. **Fine-tuning** code generation models (Qwen, CodeLlama, DeepSeek Coder, etc.)
2. **Few-shot learning** for security template generation
3. **Research** on structured code generation from specifications

## Model Recommendations

Best suited for instruction-tuned models:
- **Qwen2.5-Coder** (1.5B - 32B)
- **CodeLlama-Instruct** (7B - 34B)
- **DeepSeek-Coder-Instruct** (6.7B - 33B)
- **StarCoder2-Instruct** (3B - 15B)

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{ernest_nuclei_v3_2025,
  title={Ernest Nuclei Templates Dataset v3},
  author={OzLabs},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/OzLabs/ernest-nuclei-templates-v3}}
}
```

## License

This dataset is derived from Nuclei templates which are licensed under MIT. The dataset itself is released under MIT License.

## Changelog

### v3 (2025-01-13)
- Complete restructuring to match specification
- Added CANONICAL_RECORD with structured vulnerability data
- Added RETRIEVED_TEMPLATES for few-shot learning
- Added POC_SNIPPETS from CVE references
- JSON IR completions (no YAML, no markdown)
- Optimized similarity matching (O(n) vs O(n²))
- 11,590 training pairs (99.8% success rate)

## Contact

- **Organization**: [OzLabs](https://github.com/OzLabs)
- **Project**: [Ernest](https://github.com/OzLabs/ernest)
- **Issues**: https://github.com/OzLabs/ernest/issues

---

**Note**: This dataset is for security research and defensive purposes only. Generated templates should be validated before use in production environments.