license	sdk	emoji	colorFrom	colorTo	pinned	thumbnail	short_description
mit	docker	🚀	red	green	true	https://cdn-uploads.huggingface.co/production/uploads/670eca1fff5c46c7f23c32cd/LYKAb16YcsterJNlegp7j.png	Advanced Automated Cybersecurity Framewor

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Project Red Sword: Cybersecurity Framework

Project Red Sword is an advanced cybersecurity framework designed to address and mitigate modern cyber threats. It integrates a wide variety of security tools, including advanced attack strategies, threat intelligence sources, and AI-driven techniques for proactive defense and post-exploitation. This repository aims to provide cutting-edge techniques, automation, and integrations for both offensive and defensive cybersecurity tasks.

Project Overview

The Red Sword framework combines powerful cybersecurity tools and techniques into a single integrated platform. Some of the features include:

• AI-driven attack simulations and threat detection.
• A wide range of post-exploitation modules.
• Real-time attack and exploit automation.
• AI-powered fuzzing, exploit generation, and vulnerability scanning.
• Integration with major intelligence and FOIA sources.
• Full integration with tools like Sn1per, Empire, and custom modules for advanced penetration testing.
• Real-time threat intelligence and monitoring.
• Advanced data exfiltration techniques.
• Polymorphic and encrypted exploit payloads.

Setup

This framework requires Python 3.8+ and the following dependencies. It can be deployed easily in Hugging Face Spaces or similar environments.

Install Requirements

You can install the necessary requirements using the provided requirements.txt file.

pip install -r requirements.txt

Environment Variables

Some modules may require environment-specific credentials. You can set them up by creating a .env file or exporting them directly to your environment.

Example:

OPENAI_API_KEY=your-openai-api-key
HUGGINGFACE_API_KEY=your-huggingface-api-key

Deploying on Hugging Face Spaces

This project is designed to be deployed within Hugging Face Spaces, providing a seamless platform for model integration and AI-powered attack simulations.

1. Clone the repository:

   git clone https://huggingface.co/spaces/your-username/project-red-sword
   cd project-red-sword

2. Run the Space:

   After cloning, you can launch the project directly using the Hugging Face Space interface.

Features and Modules

The framework includes a wide array of functionalities:

1. AI-Driven Attack and Defense: Integrates with OpenAI and custom models for AI-powered cybersecurity operations.
2. Real-Time Threat Detection and Evasion: Implements automated detection and evasion strategies.
3. Post-Exploitation Modules: Includes advanced tools like keylogging, data exfiltration, and system persistence.
4. Web Scraping and Reconnaissance: Collects intelligence from public repositories and sources like FOIA.
5. Penetration Testing Modules: Integrates with Sn1per, Metasploit, and other tools for comprehensive testing.

Key Modules:

• AI Model Integrations: For attack prediction and threat intelligence.
• Post-Exploitation: Keylogging, privilege escalation, system persistence.
• Exploit Discovery: Zero-day and zero-click exploit generation.
• Custom Tools: Including a custom script generator for Hak5 devices and other third-party platforms.
• Real-Time Threat Intelligence: Provides up-to-date threat data and analysis.
• Real-Time Monitoring: Monitors data exfiltration and detects anomalies.
• Data Exfiltration: Secure data extraction techniques.
• Exploit Payloads: Polymorphic and encrypted payload generation.

Example Usage

# Example of using a custom exploit generation module
from red_sword.modules.exploits import exploit_generator

# Generate a custom exploit for a vulnerability
exploit_code = exploit_generator(target='target_system')
print(exploit_code)

Testing

The framework includes various tests, both unit and integration, to ensure everything works smoothly.

To run tests, you can use:

pytest

This will run all available tests in the tests directory and check for any issues.

Contributing

We welcome contributions to Project Red Sword. If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Clone your fork locally.
3. Create a new branch.
4. Make your changes and commit them.
5. Push your changes to your fork.
6. Open a pull request with a description of the changes you've made.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Deployment

You can deploy this framework to Hugging Face Spaces by following the Hugging Face documentation and deploying the Space via the Hugging Face platform.

1. Go to Hugging Face Spaces.
2. Click on Create New Space.
3. Choose your preferred environment and language.
4. Upload the repository files.
5. Run and test the framework.

Security Considerations

This framework contains advanced attack and penetration testing features, including exploit generation and post-exploitation modules. It should only be used in controlled environments for ethical and legal testing purposes. Always ensure compliance with local laws and regulations regarding cybersecurity.

---

References:

• OpenAI API: https://beta.openai.com/docs
• Hugging Face Spaces: https://huggingface.co/spaces
• Project Red Sword: This framework is a continuation of best practices in cybersecurity, focusing on automation, AI integration, and exploit management.

---

If you encounter any issues or need further support, please open an issue on the GitHub repository or reach out to us via the Hugging Face Space contact form.

---

Detailed Setup and Usage Instructions

Prerequisites

• Python 3.8+
• Docker (for containerized deployment)
• AWS CLI, Azure CLI, Google Cloud SDK, or DigitalOcean CLI (for cloud deployment)

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/project-red-sword.git
   cd project-red-sword

2. Install Python dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   Create a .env file in the root directory and add your API keys:

   OPENAI_API_KEY=your-openai-api-key
   HUGGINGFACE_API_KEY=your-huggingface-api-key

Running the Application

To run the application locally, use the following command:

python app.py

Docker Deployment

1. Build the Docker image:

   docker build -t project-red-sword .

2. Run the Docker container:

   docker run -p 7860:7860 project-red-sword

Cloud Deployment

AWS Deployment

1. Build the Docker image:

   docker build -t project-red-sword .

2. Push the Docker image to AWS ECR:

   aws ecr get-login-password --region YOUR_AWS_REGION | docker login --username AWS --password-stdin YOUR_AWS_ACCOUNT_ID.dkr.ecr.YOUR_AWS_REGION.amazonaws.com
   aws ecr create-repository --repository-name project-red-sword || echo "Repository already exists."
   docker tag project-red-sword:latest YOUR_AWS_ACCOUNT_ID.dkr.ecr.YOUR_AWS_REGION.amazonaws.com/project-red-sword
   docker push YOUR_AWS_ACCOUNT_ID.dkr.ecr.YOUR_AWS_REGION.amazonaws.com/project-red-sword

3. Deploy to AWS Elastic Beanstalk:

   eb init -p docker project-red-sword --region YOUR_AWS_REGION
   eb create project-red-sword-env

Azure Deployment

1. Build the Docker image:

   docker build -t project-red-sword .

2. Push the Docker image to Azure ACR:

   az acr login --name YOUR_AZURE_ACR_NAME
   az acr create --resource-group YOUR_RESOURCE_GROUP --name YOUR_AZURE_ACR_NAME --sku Basic || echo "Registry already exists."
   docker tag project-red-sword:latest YOUR_AZURE_ACR_NAME.azurecr.io/project-red-sword
   docker push YOUR_AZURE_ACR_NAME.azurecr.io/project-red-sword

3. Deploy to Azure App Service:

   az webapp create --resource-group YOUR_RESOURCE_GROUP --plan YOUR_APP_SERVICE_PLAN --name YOUR_APP_NAME --deployment-container-image-name YOUR_AZURE_ACR_NAME.azurecr.io/project-red-sword:latest

Google Cloud Deployment

1. Build the Docker image:

   docker build -t project-red-sword .

2. Push the Docker image to Google Container Registry:

   gcloud auth configure-docker
   docker tag project-red-sword gcr.io/YOUR_PROJECT_ID/project-red-sword
   docker push gcr.io/YOUR_PROJECT_ID/project-red-sword

3. Deploy to Google Kubernetes Engine:

   kubectl apply -f google-k8s.yaml

DigitalOcean Deployment

1. Build the Docker image:

   docker build -t project-red-sword .

2. Deploy to DigitalOcean:

   doctl auth init
   doctl apps create --spec digitalocean-app.yaml

Recent Updates and Changes

New Dashboards and Functionalities

We have recently added several new dashboards and functionalities to the Project Red Sword framework. These updates include:

1. Advanced Connection Methods: Added a new dashboard for managing advanced connection methods, including reverse shells and other advanced techniques.
2. Real-Time Threat Intelligence: Enhanced the real-time threat intelligence dashboard with new visualizations and data sources.
3. Predictive Analytics: Added a new dashboard for predictive analytics, utilizing machine learning algorithms to predict potential threats and vulnerabilities.
4. Automated Incident Response: Developed a new dashboard for automated incident response, allowing for quick response and containment of security incidents.
5. AI Red Teaming: Integrated AI-powered red teaming capabilities into a new dashboard, enabling advanced attack simulations and vulnerability identification.
6. Blockchain Logger: Added a new dashboard for blockchain-based logging, providing immutable logs and audit trails for security events and incidents.
7. Advanced Decryption: Developed a new dashboard for advanced decryption capabilities, allowing for secure decryption of sensitive data.
8. Advanced Malware Analysis: Enhanced the advanced malware analysis dashboard with new tools and techniques for analyzing and reverse engineering malware.
9. Advanced Social Engineering: Added a new dashboard for advanced social engineering attacks, including phishing, spear phishing, and whaling attacks.
10. Alerts and Notifications: Developed a new dashboard for managing alerts and notifications, providing real-time updates on security events and incidents.
11. APT Simulation: Added a new dashboard for simulating advanced persistent threats (APTs), allowing for comprehensive testing of the framework's defenses.
12. Cloud Exploitation: Enhanced the cloud exploitation dashboard with new tools and techniques for exploiting vulnerabilities in cloud environments.
13. Custom Dashboards: Developed customizable dashboards to provide tailored security insights and metrics.
14. Dark Web Scraper: Added a new dashboard for scraping and indexing the dark web, providing valuable intelligence on emerging threats and vulnerabilities.
15. Data Exfiltration: Enhanced the data exfiltration dashboard with new techniques for secure data extraction.
16. Data Visualization: Developed new visualizations for data analysis, including charts, graphs, and status indicators.
17. Device Fingerprinting: Added a new dashboard for device fingerprinting, allowing for the collection and analysis of device information.
18. Exploit Payloads: Enhanced the exploit payloads dashboard with new techniques for generating polymorphic and encrypted payloads.
19. Fuzzing Engine: Added a new dashboard for the fuzzing engine, allowing for comprehensive fuzz testing of targets.
20. IoT Exploitation: Enhanced the IoT exploitation dashboard with new tools and techniques for exploiting vulnerabilities in IoT devices.
21. Machine Learning AI: Developed a new dashboard for machine learning AI, providing advanced capabilities for threat detection and analysis.
22. MITM Stingray: Added a new dashboard for managing MITM Stingray operations, including interception and analysis of network traffic.
23. Network Exploitation: Enhanced the network exploitation dashboard with new tools and techniques for exploiting network vulnerabilities.
24. Vulnerability Scanner: Added a new dashboard for the vulnerability scanner, providing comprehensive scanning and reporting of vulnerabilities.
25. Wireless Exploitation: Enhanced the wireless exploitation dashboard with new tools and techniques for exploiting wireless vulnerabilities.
26. Zero Day Exploits: Added a new dashboard for managing zero-day exploits, including identification and deployment of exploits.

Huggingface Deployment Automation

To automate the deployment process for Huggingface, follow these steps:

1. Create a deployment script: Add a script named scripts/deploy_huggingface.sh that includes the necessary commands to deploy your application to Huggingface. Ensure the script includes commands to authenticate with Huggingface, upload your model or dataset, and any other necessary steps.

2. Define environment variables: Define the necessary environment variables for Huggingface deployment, such as HUGGINGFACE_API_KEY. You can set these environment variables in your deployment script or in your GitHub Actions workflow file.

3. Integrate with GitHub Actions: Integrate the Huggingface deployment script with GitHub Actions by creating a workflow file named .github/workflows/deploy_huggingface.yml. Define the necessary jobs and steps in the workflow file to run the deployment script. Ensure that the workflow file includes steps to set up the environment, install dependencies, and run the deployment script.

4. Install dependencies: Ensure that all dependencies required for the Huggingface deployment script are installed. You can specify the dependencies in a requirements.txt file or directly in the deployment script.

5. Create and manage API keys: Learn about creating and managing API keys for Huggingface by referring to the Huggingface documentation. Store the API keys securely using GitHub Secrets or other secure methods. Ensure that the API keys are not exposed in your code or version control system.

By following these steps, you can automate the deployment process for Huggingface and ensure a smooth and efficient deployment of your application.