🛡️ AI-Security-Knowledge-Base

This is a systematic AI security research project, covering a complete knowledge system from foundational machine learning algorithm theory to cutting-edge large model injection attacks, AI evasion countermeasures, and Deepfake.

🏗️ Knowledge System Architecture

The content of this repository is divided into four major sections: AI Core Algorithms, Threat Modeling, Red Team Offense, and Blue Team Defense.

🧬 Part One: AI Fundamentals and Theory

A deep understanding of the mathematical logic behind models is the prerequisite for finding algorithmic vulnerabilities.

AI Introduction - The history of artificial intelligence, its branches, and core concepts.
Deep Learning Architectures - In-depth deconstruction from CNN/RNN to Transformer architectures.
Core Machine Learning Algorithms - Linear/logistic regression, SVM, decision trees, random forests, and Naive Bayes.
Unsupervised Learning Exploration - Cluster analysis (K-Means, GMM) and association rules.
Reinforcement Learning Principles - Agent game logic and the foundations of automated decision-making.

🚨 Part Two: Threat Modeling and Risk Frameworks

Analyze the attack surface of AI systems based on mainstream international security standards.

AI Security Risk Framework - In-depth interpretation of the OWASP ML/LLM Top 10, covering Prompt Injection, data poisoning, and model inversion.
MCP Specialized Security - The latest security research on the Model Context Protocol, including tool poisoning and Cursor IDE vulnerability analysis.
skill Security - Analysis of skill security configuration and defense practices.

🏹 Part Three: Red Team Offensive Drills

Explore the practical use of AI as a "weapon" in cyber offense and defense.

Offensive AI in Practice -
- AI Vulnerability Scanning: Using Shennina and Shannon to achieve reasoning-based automated penetration testing.
- Malware Countermeasures: Using Pesidious and MalwareGAN for reinforcement learning-driven evasion mutation.
- Deepfake Fraud: The principles and exploitation of real-time audio-video cloning (Deep-Live-Cam, Voice-Pro).

🛡️ Part Four: Blue Team Defense Empowerment

Research how to use algorithms to build a more predictive security defense system.

Algorithm-Empowered Security - Machine learning-based anomalous traffic detection, automated auditing, and threat hunting.

🚀 Core Focus Areas

Focus Area	Core Technologies	Key Tools
LLM Security	Prompt Injection, Excessive Agency	Gitleaks, Prompt-Guard
Adversarial ML	Evasion Attacks, Data Poisoning	Pesidious, ART (Adversarial Robustness Toolbox)
Identity Fraud	Deepfake, Voice Cloning	Deep-Live-Cam, RVC-WebUI
Automation	Autonomous Pentesting Agents	Shannon, MetaGPT

🛠️ Suggested Reading Approach

Beginners: It is recommended to start with AI.md and read the algorithm modules in order to build underlying mathematical intuition.
Security Practitioners: Go directly to AI安全.md to understand the OWASP risk framework, and refer to 进攻性AI.md for local experiments.
Developers: Focus on reading MCP安全.md and 算法赋能安全.md to build secure AI integration solutions.

Complete Video Course:

More powerful and practical code, more advanced vulnerability exploitation methods, more approaches!

⚠️ Disclaimer

Important Notice: The technical content covered in this repository is intended solely for security education, compliance auditing, and academic research.

It is prohibited to use any technology in this project for illegal purposes.
Before using offensive tools, please ensure that you have obtained explicit authorization for the target system.
The author is not responsible for any losses caused by misuse of the content in this repository.

🤝 Contribution and Communication

Issues or Pull Requests to improve the documentation are welcome. If you find this project helpful, please give it a Star 🌟.

Last Updated: 2026-03-06
Author: GhostWolfLab/Snowwolf

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