Project-Red-Sword - Defense Intelligence Agency's Special Access Progr

项目说明

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license: mit
sdk: docker
emoji: 🚀
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colorTo: green
pinned: true
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short_description: Ad Automated Cybersecurity Framewor
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Classified Materials Warning

WARNING: This repository contains classified materials. Unauthorized access, use, disclosure, or dissemination of the information contained herein is strictly prohibited and may result in severe legal consequences.

DISCLAIMER: All users must adhere to the handling and usage guidelines for classified materials as outlined by the relevant authorities. Failure to comply with these guidelines may result in disciplinary action, including termination of access and legal prosecution.

NOTICE: Unauthorized access or misuse of classified information is a of federal law and may result in criminal prosecution. All acti within this repository are monitored and logged.

Project Red Sword: Cybersecurity Framework

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

Project O

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

Setup

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

Install Requirements

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

En

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

Example:

Deploying on Hugging Face Spaces

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

1. Clone the repository:

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-Dri and Defense: Integrates with OpenAI and custom models for AI-powered cybersecurity operations.
2. Real-Time Threat Detection and E: Implements automated detection and e strategies.
3. Post-Exploitation Modules: Includes ad 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 comprehensi.

Key Modules:

Example Usage

Testing

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

To run tests, you can use:

This will run all a 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 en and language.
4. Upload the repository files.
5. Run and test the framework.

Security Considerations

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

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References:

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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.

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Detailed Setup and Usage Instructions

#### Prerequisites

#### Installation

1. Clone the repository:

2. Install Python dependencies:

3. Set up en :

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

#### Running the Application

To run the application locally, use the following command:

#### Docker Deployment

1. Build the Docker image:

2. Run the Docker container:

#### Cloud Deployment

##### AWS Deployment

1. Build the Docker image:

2. Push the Docker image to AWS ECR:

3. Deploy to AWS Elastic Beanstalk:

##### Azure Deployment

1. Build the Docker image:

2. Push the Docker image to Azure ACR:

3. Deploy to Azure App Service:

##### Google Cloud Deployment

1. Build the Docker image:

2. Push the Docker image to Google Container Registry:

3. Deploy to Google Kubernetes Engine:

##### DigitalOcean Deployment

1. Build the Docker image:

2. Deploy to DigitalOcean:

Recent Updates and Changes

New Dashboards and Functionalities

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

1. Ad Connection Methods: Added a new dashboard for managing ad connection methods, including reverse shells and other ad techniques.
2. Real-Time Threat Intelligence: Enhanced the real-time threat intelligence dashboard with new and data sources.
3. Predicti: Added a new dashboard for predicti, utilizing machine learning algorithms to predict potential threats and .
4. Automated Incident Response: De 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 ad attack simulations and identification.
6. Blockchain Logger: Added a new dashboard for blockchain-based logging, pro immutable logs and audit trails for security events and incidents.
7. Ad Decryption: De a new dashboard for ad decryption capabilities, allowing for secure decryption of sensitive data.
8. Ad Malware Analysis: Enhanced the ad malware analysis dashboard with new tools and techniques for analyzing and reverse engineering malware.
9. Ad Social Engineering: Added a new dashboard for ad social engineering attacks, including phishing, spear phishing, and whaling attacks.
10. Alerts and Notifications: De a new dashboard for managing alerts and notifications, pro real-time updates on security events and incidents.
11. APT Simulation: Added a new dashboard for simulating ad persistent threats (APTs), allowing for comprehensi of the framework's defenses.
12. Cloud Exploitation: Enhanced the cloud exploitation dashboard with new tools and techniques for exploiting in cloud en.
13. Custom Dashboards: De customizable dashboards to provide tailored security insights and metrics.
14. Dark Web Scraper: Added a new dashboard for scraping and indexing the dark web, pro intelligence on emerging threats and .
15. Data Exfiltration: Enhanced the data exfiltration dashboard with new techniques for secure data extraction.
16. Data : De new 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 in IoT devices.
21. Machine Learning AI: De a new dashboard for machine learning AI, pro ad 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 .
24. Scanner: Added a new dashboard for the scanner, pro comprehensi and reporting of .
25. Wireless Exploitation: Enhanced the wireless exploitation dashboard with new tools and techniques for exploiting wireless .
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 en : Define the necessary en for Huggingface deployment, such as HUGGINGFACE_API_KEY. You can set these en 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 en, 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 control system.

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

项目信息

开发语言：Python

Star 数：25

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Defense Intelligence Agency's Special Access Program For Cyber Intelligence, A National Security Asset | Project Red Sword Deploys an AI-Operated, Offensive & Defensive Cyber Espionage & State Sponsored Attack Framework With Automated Red, Blue & Purple Team, Auditing & Reporting Capabilties.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。