README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - en
license: mit
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-generation
  - question-answering
task_ids:
  - language-modeling
  - language-modeling
pretty_name: Bug Bounty Hunter
tags:
  - cybersecurity
  - vulnerability
  - bug-bounty
  - security
  - instruction-tuning
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
      - split: validation
        path: data/validation-00000-of-00001.parquet
      - split: test
        path: data/test-00000-of-00001.parquet

Bug Bounty Hunter v1 - Fine-Tuning Dataset

The largest public dataset for training AI models on bug bounty hunting expertise.

Overview

52,743 instruction-response pairs covering 40+ vulnerability types across 6 categories, extracted from real-world bug bounty writeups and security research.

Dataset Summary

This dataset is designed to fine-tune language models to become expert bug bounty hunters. Each example contains a technical instruction and a detailed response drawn from thousands of real vulnerability writeups, security blogs, CVE analyses, and methodology guides.

Use Cases

• Fine-tune chat models for bug bounty expertise
• Train vulnerability classifiers
• Teach LLMs security testing methodology
• Build automated security tools

Dataset Structure

Split	Examples	Description
train	42,194	Training set (80%)
validation	5,274	Validation set (10%)
test	5,275	Test set (10%)

Data Format (ChatML)

{
  "messages": [
    {"role": "system", "content": "You are an expert bug bounty hunter..."},
    {"role": "user", "content": "Explain XSS in detail."},
    {"role": "assistant", "content": "Cross-Site Scripting (XSS) is a vulnerability..."}
  ]
}

Instruction Types

Type	Count	Description
explain	10,165	Vulnerability explanations
find	10,157	Detection techniques
exploit	10,149	Exploitation methods
prevent	10,138	Prevention & mitigation
impact	10,134	Business impact analysis
writeup_analysis	2,000	Bug report analysis

Vulnerability Types (40+)

Web Exploitation: XSS, SQL Injection, SSRF, CSRF, XXE, IDOR, Path Traversal, Command Injection, Open Redirect, File Upload, Subdomain Takeover, Clickjacking

API & Modern: GraphQL, API Misconfigurations, Mass Assignment, NoSQL Injection, JWT Attacks, OAuth Flaws, Cache Attacks, Host Header

Advanced: RCE, Deserialization, Template Injection, Request Smuggling, Race Conditions, Business Logic

Reconnaissance: Subdomain Discovery, Content Discovery, JS Source Analysis, Port Scanning, Technology Fingerprinting, Cloud Recon

Methodology: Burp Suite, Automation, Mobile Testing, Cloud Testing, IoT Firmware

Load with HuggingFace

from datasets import load_dataset

dataset = load_dataset("your-username/bugbounty-hunter-v1")
print(dataset)
# DatasetDict({
#     train: Dataset({ features: ['system', 'user', 'assistant', 'messages_json'], num_rows: 42194 })
#     validation: Dataset({ features: [...], num_rows: 5274 })
#     test: Dataset({ features: [...], num_rows: 5275 })
# })

# Format for chat models
def format_chat(example):
    return {"text": f"System: {example['system']}\nUser: {example['user']}\nAssistant: {example['assistant']}"}

Load with Transformers

from transformers import AutoTokenizer
from datasets import load_dataset

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
dataset = load_dataset("your-username/bugbounty-hunter-v1")

def format_for_training(examples):
    texts = []
    for sys, user, asst in zip(examples["system"], examples["user"], examples["assistant"]):
        messages = [
            {"role": "system", "content": sys},
            {"role": "user", "content": user},
            {"role": "assistant", "content": asst},
        ]
        texts.append(tokenizer.apply_chat_template(messages, tokenize=False))
    return tokenizer(texts, truncation=True, max_length=2048)

dataset = dataset.map(format_for_training, batched=True)

Source Data

Built from a massive collection of 6.32 GB / 17,014 files:

• 200,000 CVEs from NVD
• 4,200+ full bug bounty writeups (HTML)
• 6,421 PentesterLand indexed writeups
• 3,300+ HackerOne reports
• 35+ security research blogs
• 164+ wordlists (323+ MB)
• OWASP guides, exploit databases, CTF writeups

Model Training

For training, see our training scripts:

• LoRA with Unsloth: Training/train_unsloth.py
• QLoRA with HF: Training/qlora_config.json
• Full fine-tune: Training/full_ft_config.json

Benchmark

Evaluate your model with AI-Datasets/bug_bounty_benchmark.json (102 curated questions across 20+ categories).

Citation

@dataset{bugbounty-hunter-v1,
  title        = {Bug Bounty Hunter v1 - Fine-Tuning Dataset},
  author       = {Bug Bounty Community},
  year         = {2026},
  description  = {52,743 instruction-response pairs for bug bounty hunting expertise},
  url          = {https://huggingface.co/datasets/your-username/bugbounty-hunter-v1},
  license      = {MIT}
}