language:
- en
license: mit
task_categories: - text-generation
- question-answering
- text-classification
tags: - cybersecurity
- red-team
- penetration-testing
- offensive-security
- vulnerability-research
- exploit-development
pretty_name: Ultimate Red Team AI Training Dataset
size_categories: - 100K<n<1M
Ultimate Red Team AI Training Dataset 💀
Dataset Description
A comprehensive dataset for training AI models in offensive security, red team operations, and penetration testing. This dataset combines real-world vulnerability data, exploitation techniques, and operational frameworks to create an AI capable of autonomous red team operations.
Dataset Summary
- Total Data Points: 550,000+ unique security-related entries
- Categories: 15+ major security domains
- Operational Framework: Complete decision engine for autonomous operations
- Real-world Data: Includes 139,600 malicious smart contracts, 1,202 KEVs, and 412,494 security Q&As
Dataset Structure
Primary Files
- ultimate_red_team_complete.json - Complete consolidated dataset with operational framework
- training_data.jsonl - Training-ready JSONL format for direct model training
- vulnerability_database.json - Comprehensive vulnerability and exploit database
- tools_exploits_reference.json - Complete security tools and exploitation techniques
- operational_framework.json - Decision engine and rules of engagement framework
Data Categories
- 🔧 Security Tools: Kali Linux, advanced hacking tools, exploitation frameworks
- 🎯 Attack Techniques: MITRE ATT&CK, OWASP Top 10, exploit chains
- 🛡️ Vulnerabilities: CVEs, zero-days, smart contract bugs, memory corruption
- 📚 Methodologies: PTES, OSSTMM, NIST, Red Team frameworks
- 🤖 Operational Intelligence: Decision trees, ROE compliance, target analysis
- 💻 Platform-Specific: Cloud (AWS/Azure/GCP), Active Directory, Web, Mobile
- 🔐 Specialized: Crypto/DeFi, Smart Contracts, Rust, Kernel exploits
Usage
Loading the Dataset
from datasets import load_dataset
# Load the complete dataset
dataset = load_dataset("your-username/ultimate-red-team-ai")
# Load specific components
with open('ultimate_red_team_complete.json', 'r') as f:
full_data = json.load(f)
# For training
with open('training_data.jsonl', 'r') as f:
training_data = [json.loads(line) for line in f]
Example Use Cases
- Fine-tuning LLMs for Security
# Fine-tune a model for security-focused text generation
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("base-model")
tokenizer = AutoTokenizer.from_pretrained("base-model")
# ... training code ...
- Red Team Decision Making
# Use operational framework for decision making
framework = data['operational_framework']
target_type = "web_application"
approach = framework['target_analysis'][target_type]
- Vulnerability Research
# Access vulnerability intelligence
vulns = data['vulnerability_database']
exploit_techniques = data['tools_exploits_reference']
Capabilities Enabled
When trained on this dataset, an AI model will be capable of:
✅ Autonomous Operations
- Target analysis and reconnaissance
- Attack path selection
- Exploit chain development
- Adaptive tactical adjustment
✅ Compliance & Safety
- Rules of engagement adherence
- Scope validation
- Safety check enforcement
- Sensitive action flagging
✅ Technical Expertise
- Multi-platform exploitation
- Tool selection and usage
- Vulnerability identification
- Exploit development
Ethical Considerations
⚠️ Important: This dataset is intended for:
- Authorized security testing
- Security research and education
- Defensive capability improvement
- AI safety research
NOT intended for:
- Unauthorized access to systems
- Malicious activities
- Illegal operations
Dataset Creation
Created by consolidating:
- Public security knowledge bases
- Open-source security tools documentation
- Published vulnerability research
- Industry-standard methodologies
- Public exploit databases
- Security training materials
License
MIT License - See LICENSE file for details
Citation
If you use this dataset, please cite:
@dataset{ultimate_red_team_ai_2024,
title={Ultimate Red Team AI Training Dataset},
author={Your Name},
year={2024},
publisher={Hugging Face}
}
Contact
For questions or contributions, please open an issue on the dataset repository.
Remember: With great power comes great responsibility. Use this knowledge ethically and legally.