Offensive-Defensive-AI-Framework-for-CyberSecurity - A research framework exploring offensive and defen

项目说明

Offensive–Defensive AI Framework for CyberSecurity

*A Dual Multi-Agent AI Architecture for Modern Cyber Operations*

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O

This repository contains the complete research, LaTeX source, diagrams, and proof-of-concept (PoC) implementations for a dual-paradigm cybersecurity study:

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1. Offensi – LRMAM

LRMAM — Large Remote Multi-Agent Malware
A conceptual model describing how future malware may evolve by delegating intelligence to external AI multi-agent systems, using remote LLMs, adapti templates, and distributed orchestration.

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2. Defensi – DMAS

DMAS — Defensi AI System
A two-layer cyber defence architecture consisting of distributed Edge-Agents and a centralised Core-Agent Swarm performing structured AI reasoning, correlation, and policy-go mitigation.

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Together, these works form a unified research perspective on the next generation of AI-nati, where both attackers and defenders use autonomous multi-agent AI systems.

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Purpose of This Repository

This repository is intended for:

> All materials here are scientific, safe, and non-destructive.

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Ethics & Safety

This repository does not contain malware or harmful code.

All PoC implementations:

This project adheres to:

> The offensi (LRMAM) is presented for academic awareness, not operational use.

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项目信息

开发语言：Jupyter Notebook

Star 数：1

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A research framework exploring offensive and defensive AI multi-agent architectures for modern cybersecurity. Includes two scientific papers, system designs, and safe PoC implementations.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。