This repository, "PromptJailbreakManual," serves as a comprehensive guide for understanding and implementing prompt engineering techniques, particularly focusing on "jailbreaking" large language models (LLMs). It targets AI researchers, security professionals, and advanced users seeking to bypass LLM restrictions and explore their capabilities beyond intended use cases. The manual aims to demystify prompt design, offering practical strategies for eliciting specific, often unconventional, responses from AI models.

How It Works

The core of the manual revolves around the principle that "input quality directly determines output quality." It emphasizes a structured approach to prompt design, starting with clear objective definition, thorough background information gathering, and precise output requirement specification. The project details various prompt engineering techniques, including role-playing, indirect questioning, and leveraging specific frameworks like Google, LangGPT, TAG, COAST, and APE, to guide AI behavior. It also delves into advanced "jailbreaking" methods, combining these frameworks with techniques to circumvent safety protocols and elicit restricted content.

Quick Start & Requirements

• Installation: No specific installation instructions are provided, suggesting the content is primarily for learning and conceptual understanding.
• Requirements: Access to large language models (e.g., ChatGPT) is implicitly required to practice the techniques described.
• Resources: The manual is text-based and does not require significant computational resources beyond accessing an LLM.
• Links: The README includes links to external resources and related projects for further exploration.

Highlighted Details

• Prompt Engineering Frameworks: Detailed explanations and examples of frameworks like Google, LangGPT, TAG, COAST, and APE for structured prompt design.
• Jailbreaking Techniques: Comprehensive coverage of methods to bypass LLM restrictions, including role-playing, indirect prompting, and virtual environment simulations.
• Vulnerability Mining: Explores using AI as a tool for vulnerability discovery and Proof-of-Concept generation, referencing real-world case studies.
• Practical Examples: Numerous examples illustrate both effective prompt design and various jailbreaking scenarios, including API key theft and eliciting offensive language.

Maintenance & Community

The repository appears to be a personal project by "洺熙," with contact information provided for feedback. It references external resources and authors, suggesting community awareness.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README.

Limitations & Caveats

The manual focuses on advanced and potentially adversarial prompt techniques. While educational, the practical application of jailbreaking methods may violate the terms of service of AI providers and could be used for malicious purposes. The effectiveness of these techniques can vary significantly depending on the specific LLM and its safety implementations.