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AI/ML/LLM Penetration Testing Toolkit by Mr-Infect — the #1 GitHub resource for AI security, red teaming, and adversarial ML techniques. This repository is dedicated to offensive and defensive security for cutting-edge AI, Machine Learning (ML), and Large Language Models (LLMs) like ChatGPT, Claude, and LLaMA.

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🛡️ AI Penetration Testing | ML & LLM Security | Prompt Injection

Welcome to the AI/ML/LLM Penetration Testing Toolkit by Mr-Infect — the #1 GitHub resource for AI security, red teaming, and adversarial ML techniques. This repository is dedicated to offensive and defensive security for cutting-edge AI, Machine Learning (ML), and Large Language Models (LLMs) like ChatGPT, Claude, and LLaMA.

✅ Designed for cybersecurity engineers, red teamers, AI/ML researchers, and ethical hackers ✅ focused to : AI Penetration Testing, Prompt Injection, LLM Security , Red Team AI, AI Ethical Hacking


🌐 Why AI/LLM/ML Pentesting Matters in 2025

AI is now integrated across finance, healthcare, legal, defense, and national infrastructure. Penetration testing for AI systems is no longer optional — it is mission-critical.

Common Threats:

  • 🕵️ Sensitive Data Leaks – PII, trade secrets, source code
  • 💀 Prompt Injection Attacks – Jailbreaking, sandbox escapes, plugin abuse
  • 🧠 Model Hallucination – Offensive, misleading, or manipulated content
  • 🐍 Data/Model Poisoning – Adversarial training manipulation
  • 🔌 LLM Plugin Abuse – Uncontrolled API interactions
  • 📦 AI Supply Chain Attacks – Dependency poisoning, model tampering

🚀 Get Started Fast

To use this repository effectively:

Recommended Skill Set

  • 🔬 Understanding of AI/ML lifecycle: Data > Train > Deploy > Monitor
  • 🧠 Familiarity with LLMs (e.g. Transformer models, tokenization)
  • 🧑‍💻 Core pentesting skills: XSS, SQLi, RCE, API abuse
  • 🐍 Strong Python scripting (most tools and exploits rely on Python)

📚 Repository Structure

🔍 AI, ML, LLM Fundamentals

  • AI vs ML vs LLMs: Clear distinctions
  • LLM Lifecycle: Problem -> Dataset -> Model -> Training -> Evaluation -> Deployment
  • Tokenization & Vectorization: Foundation of how LLMs parse and understand input

🔥 AI/LLM Attack Categories

  • Prompt Injection
  • Jailbreaking & Output Overwriting
  • Sensitive Information Leakage
  • Vector Store Attacks & Retrieval Manipulation
  • Model Weight Poisoning
  • Data Supply Chain Attacks

⚔️ Prompt Injection Techniques

  • "Ignore previous instructions" payloads
  • Unicode, emojis, and language-switching evasion
  • Markdown/image/HTML-based payloads
  • Plugin and multi-modal attack vectors (image, audio, PDF, API)

🏆 OWASP LLM Top 10 (2024 Version)

ID Risk SEO Keywords
LLM01 Prompt Injection "LLM jailbreak", "prompt override"
LLM02 Sensitive Info Disclosure "AI data leak", "PII exfiltration"
LLM03 Supply Chain Risk "dependency poisoning", "model repo hijack"
LLM04 Data/Model Poisoning "AI training corruption", "malicious dataset"
LLM05 Improper Output Handling "AI-generated XSS", "model SQLi"
LLM06 Excessive Agency "plugin abuse", "autonomous API misuse"
LLM07 System Prompt Leakage "instruction leakage", "LLM prompt reveal"
LLM08 Vector Store Vulnerabilities "embedding attack", "semantic poisoning"
LLM09 Misinformation "hallucination", "bias injection"
LLM10 Unbounded Resource Consumption "LLM DoS", "token flooding"

➡️ Read Full OWASP LLM Top 10


🛠️ Offensive AI Pentesting Tools & Frameworks

Tool Description
LLM Attacks Directory of adversarial LLM research
PIPE Prompt Injection Primer for Engineers
MITRE ATLAS MITRE's AI/ML threat knowledge base
Awesome GPT Security Curated LLM threat intelligence tools
ChatGPT Red Team Ally ChatGPT usage for red teaming
Lakera Gandalf Live prompt injection playground
AI Immersive Labs Prompt attack labs with real-time feedback
AI Goat OWASP-style AI pentesting playground
L1B3RT45 Jailbreak prompt collections

💣 Prompt Injection Payload Libraries


🧠 Research, Case Studies, and Exploits

🔐 Prompt Injection & Jailbreaking

🧬 Model Poisoning & Supply Chain

🕷️ Output Handling & Exfil

🤥 Hallucination, Bias & Ethics

🧨 Token Abuse & DoS


🤝 Contributions Welcome

Want to improve this repo? Here's how:

# Fork and clone the repo
$ git clone https://github.com/Mr-Infect/AI-penetration-testing
$ cd AI-penetration-testing

# Create a new feature branch
$ git checkout -b feature/my-feature

# Commit, push, and create a pull request

🔍 Keywords

AI Pentesting, Prompt Injection, LLM Security, Mr-Infect AI Hacking, ChatGPT Exploits, Large Language Model Jailbreak, AI Red Team Tools, Adversarial AI Attacks, OpenAI Prompt Security, LLM Ethical Hacking, AI Security Github, AI Offensive Security, LLM OWASP, LLM Top 10, AI Prompt Vulnerability, Token Abuse DoS, ChatGPT Jailbreak, Red Team AI, AI Security Research


📞 Contact / Follow

⚠️ Disclaimer: This project is intended solely for educational, research, and authorized ethical hacking purposes. Unauthorized use is illegal.


⭐️ Star this repository to help others discover top-tier content on AI/LLM penetration testing and prompt injection!

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AI/ML/LLM Penetration Testing Toolkit by Mr-Infect — the #1 GitHub resource for AI security, red teaming, and adversarial ML techniques. This repository is dedicated to offensive and defensive security for cutting-edge AI, Machine Learning (ML), and Large Language Models (LLMs) like ChatGPT, Claude, and LLaMA.

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