This repository, "Learn-Prompt-Hacking," provides a comprehensive educational resource for understanding and mastering prompt engineering and prompt hacking techniques for Large Language Models (LLMs). It is designed for AI developers, data scientists, and security professionals seeking to enhance their LLM interaction capabilities while fortifying against emerging security threats. The primary benefit is equipping users with advanced knowledge to build more effective and secure GenAI applications.

How It Works

The course material covers three core pillars: Prompt Engineering Technology, GenAI Development Technology, and Prompt Hacking Technology. It systematically explores offensive techniques like ChatGPT jailbreaks, GPT Assistants prompt leaks, and GPTs prompt injection, alongside defensive strategies for LLM security. The approach emphasizes understanding adversarial machine learning principles to build robust LLM security defense technologies and mitigation strategies.

Quick Start & Requirements

This repository functions as a course outline and resource collection, not a software project with installation or execution requirements. Therefore, no primary install commands, prerequisites, or setup time estimates are applicable.

Highlighted Details

• Comprehensive coverage of prompt hacking techniques, including jailbreaks, prompt injection, and prompt leaks across various GPT models.
• Detailed exploration of LLM security defense technologies and adversarial machine learning.
• Resources include foundational NLP advancements, LLM hacking tools, security papers, and conference slides.
• Focuses on both the offensive (hacking) and defensive (security) aspects of prompt engineering.

Maintenance & Community

The provided README does not contain information regarding project maintenance, notable contributors, sponsorships, or community channels such as Discord or Slack.

Licensing & Compatibility

No specific license type is mentioned in the README, nor are there any compatibility notes for commercial use or integration with closed-source projects.

Limitations & Caveats

As an educational resource, this repository lacks executable code, practical implementation guides, or runnable examples. Its primary value lies in theoretical knowledge and curated links, making it unsuitable for direct adoption as a software tool. Users seeking hands-on implementation will need to develop their own code based on the course material.