AI Systems Transparency and Observability for All
CL4R1T4S is a comprehensive repository of extracted system prompts, guidelines, and operational tools from leading AI models and agents. It aggregates data from major AI systems including OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, xAI’s Grok, Perplexity, Cursor, Windsurf, Devin, Manus, Replit, and others. The project aims to provide transparency into the unseen instructions that govern AI behavior, enabling users to understand the underlying mechanisms shaping model responses.
By making these prompts openly available, CL4R1T4S seeks to demystify how AI systems are designed to operate, including restrictions on content, enforced personas, ethical frameworks, and political biases embedded within their architecture.
How It Works
The project relies on community-driven efforts to extract, reverse-engineer, or leak system prompts from various AI models. Contributors are encouraged to submit their findings through pull requests on the GitHub repository, providing details such as the model name, version, extraction date, and contextual notes.
This structured approach facilitates comparative analysis, helping researchers identify patterns in AI behavior, ethical constraints, or potential biases.
Use Cases
- Red Teaming and Security Testing: Security researchers and ethical hackers use CL4R1T4S to identify vulnerabilities in AI systems by examining the underlying instructions that govern their responses. This includes detecting hardcoded restrictions, ethical safeguards, or potential loopholes that could be exploited.
- Bias and Fairness Audits: By analyzing system prompts, researchers can assess whether AI models are designed with inherent biases or ethical frameworks that may influence their outputs.
- Model Comparison and Benchmarking: Developers and AI engineers leverage CL4R1T4S to compare the operational guidelines of different AI systems.
- Educational and Research Purposes: Academics and students use the repository to study the inner workings of AI systems, gaining insights into how prompts shape model responses.
- Public Awareness and Advocacy: The project serves as a resource for journalists, policymakers, and advocacy groups seeking to inform the public about the hidden mechanisms behind AI systems. Transparency in this area is critical for fostering trust and ensuring that AI technologies align with societal values.
Key Features and Capabilities
- Comprehensive Model Coverage: CL4R1T4S includes system prompts from virtually all major AI models and agents, providing a centralized resource for researchers and practitioners.
- Community-Driven Contributions: The project thrives on contributions from the global community, including reverse-engineered prompts, leaked documents, and user-submitted findings. This collaborative approach ensures that the repository remains up-to-date and reflective of the latest AI developments.
- Structured Data Organization: Extracted prompts are categorized and documented with metadata, such as model versions and extraction dates.
- Open-Source Tools and Methodologies: CL4R1T4S provides tools and guidelines for validating and analyzing system prompts.
- Focus on Ethical and Political Frameworks: The repository highlights how system prompts embed ethical guidelines, political biases, or content restrictions.
- Automated Red Teaming Support: While primarily a documentation effort, the structured nature of CL4R1T4S enables automated red teaming workflows. Security teams can use the repository to systematically test AI models for vulnerabilities, compliance with ethical standards, or unintended behaviors.
Notable Models and Agents Documented
- ChatGPT (OpenAI): System prompts governing response restrictions, ethical guidelines, and persona enforcement.
- Claude (Anthropic): Detailed instructions on content moderation, refusal strategies, and ethical constraints.
- Gemini (Google): Prompts related to factual accuracy, bias mitigation, and user interaction protocols.
- Grok (xAI): System-level directives on political neutrality, response formatting, and content boundaries.
- Perplexity: Guidelines for information retrieval, citation practices, and user query handling.
- Windsurf, Devin, and Manus: System instructions for specialized AI agents, including task automation, tool integration, and ethical decision-making frameworks.