Back to List
Comprehensive Repository of Leaked System Prompts Surfaces for Next-Generation AI Models Including GPT 5.5 and Claude Fable 5
Industry NewsAI SecurityLarge Language ModelsOpenAI

Comprehensive Repository of Leaked System Prompts Surfaces for Next-Generation AI Models Including GPT 5.5 and Claude Fable 5

A significant GitHub repository titled 'system_prompts_leaks' has been identified, containing extracted system prompts from a wide range of industry-leading artificial intelligence models. The collection, maintained by user asgeirtj, features internal instructions from major AI labs including OpenAI, Anthropic, and Google. Notably, the leak includes prompts for high-profile and potentially unreleased versions such as OpenAI's ChatGPT 5.5 Thinking and Anthropic's Claude Fable 5. The repository also covers specialized development tools and search platforms like GitHub Copilot, Cursor, and Perplexity. This ongoing project aims to provide a centralized source for the underlying directives that govern AI behavior across the most advanced platforms currently available in the tech landscape.

GitHub Trending

Key Takeaways

  • A specialized GitHub repository has compiled leaked system prompts from major AI developers including OpenAI, Anthropic, Google, and xAI.
  • The leak features advanced and next-generation models such as ChatGPT 5.5 Thinking, GPT 5.5 Instant, and Claude Fable 5.
  • Beyond general-purpose LLMs, the repository includes prompts for specialized coding and productivity tools like Cursor, GitHub Copilot, and Claude Code.
  • The repository is actively maintained with a commitment to regular updates as new models and prompts are extracted.

In-Depth Analysis

Extensive Coverage of Leading AI Model Families

The repository 'system_prompts_leaks' provides a detailed look into the internal configurations of several prominent AI model families. According to the documentation provided by the author, asgeirtj, the collection includes prompts extracted from Anthropic's latest suite, specifically mentioning Claude Fable 5, Opus 4.8, Claude Code, and Claude Design. These entries suggest a focus on both high-reasoning models and task-specific iterations designed for programming and creative work.

OpenAI's presence in the leak is equally significant, covering a range of models that include ChatGPT 5.5 Thinking, GPT 5.5 Instant, and the programming-centric Codex. The inclusion of '5.5' series models highlights the repository's focus on the most recent or upcoming iterations of OpenAI's generative pre-trained transformers. Additionally, Google's ecosystem is represented through the extraction of prompts from Gemini 3.5 Flash, Gemini 3.1 Pro, and a model identified as Antigravity.

Inclusion of Specialized Tools and Ecosystem Integration

The scope of the 'system_prompts_leaks' project extends beyond standalone chat interfaces to include integrated AI tools that are widely used in the software development industry. The repository lists extracted prompts for xAI's Grok, as well as popular AI-powered code editors and assistants such as Cursor, GitHub Copilot, and VS Code.

Furthermore, the leak encompasses information-retrieval platforms like Perplexity. By aggregating prompts from these diverse sources, the repository provides a broad overview of how different companies structure their system instructions to handle various tasks—ranging from general conversation and deep thinking to specialized code generation and web searching. The author notes that the repository is subject to regular updates, indicating a continuous effort to track changes in AI system instructions.

Industry Impact

The emergence of a centralized repository for extracted system prompts carries significant implications for the AI industry. System prompts serve as the foundational layer of an AI's persona and operational constraints; their exposure provides a window into the safety guardrails, formatting requirements, and behavioral instructions set by AI developers.

For the research community, such leaks offer a way to study the 'hidden' logic that governs model responses. However, for the companies involved—such as OpenAI, Anthropic, and Google—the public availability of these prompts may necessitate more robust security measures to protect proprietary instruction sets. The inclusion of models like GPT 5.5 and Claude Fable 5 suggests that even the most advanced and newest systems are being actively targeted for prompt extraction, highlighting a persistent challenge in AI security and intellectual property protection.

Frequently Asked Questions

Question: What specific Anthropic models are included in the system prompt leak?

According to the repository, the leak includes system prompts for Claude Fable 5, Opus 4.8, Claude Code, and Claude Design.

Question: Does the repository contain prompts for AI coding assistants?

Yes, the repository features extracted system prompts for several major coding tools, including Cursor, GitHub Copilot, VS Code, and Anthropic's Claude Code.

Question: Which OpenAI models are listed in the 'system_prompts_leaks' repository?

The repository lists prompts for ChatGPT 5.5 Thinking, GPT 5.5 Instant, and Codex.

Related News

Meituan Unveils LongCat-2.0: A 1.6 Trillion Parameter Model Optimized for Agentic Coding on Domestic Clusters
Industry News

Meituan Unveils LongCat-2.0: A 1.6 Trillion Parameter Model Optimized for Agentic Coding on Domestic Clusters

Meituan's technology team has officially released LongCat-2.0, a landmark large language model featuring 1.6 trillion parameters. This model distinguishes itself as the first of its scale to complete the entire training and inference lifecycle on a domestic computing cluster of 50,000 cards. Designed specifically for Agentic Coding, LongCat-2.0 supports a native 1M long-context window and was pre-trained from scratch. With a dynamic activation range between 33B and 56B (averaging 48B), the model is engineered to provide high efficiency and stability in complex code understanding, generation, and execution tasks. This release marks a significant milestone for domestic AI infrastructure and the evolution of autonomous coding agents.

Meituan Technical Team Presents Selected Academic Papers at ICML 2026 to Advance Machine Learning Research
Industry News

Meituan Technical Team Presents Selected Academic Papers at ICML 2026 to Advance Machine Learning Research

The Meituan Technical Team has announced its participation in the International Conference on Machine Learning (ICML) 2026, one of the world's most influential academic gatherings in the field. ICML 2026 serves as a critical platform for discussing the future challenges and core issues facing machine learning development. Meituan's involvement includes the presentation of selected academic papers that have been evaluated for their significant theoretical value and practical impact. By contributing to this top-tier conference, the Meituan Technical Team aims to push the boundaries of the field and help lead future research directions. This engagement highlights the team's commitment to high-quality research that addresses both the fundamental questions of machine learning and its real-world applications, reinforcing their position within the global technical community.

Meituan Fulfillment AI Team Showcases LLM-Based Agent Innovations and Self-Evolving Systems at ACL 2026
Industry News

Meituan Fulfillment AI Team Showcases LLM-Based Agent Innovations and Self-Evolving Systems at ACL 2026

The Meituan Fulfillment AI Algorithm Team has unveiled its latest advancements in Large Language Model (LLM)-based Agent technology at a special session for the ACL 2026 conference. Focused on empowering Meituan's fulfillment business, the team is developing a self-evolving Agent operating system. Their research, which has resulted in dozens of publications in top-tier venues like ACL and EMNLP, spans critical domains including Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding. This initiative represents a significant step in integrating frontier AI research with large-scale industrial fulfillment operations, aiming to enhance efficiency and system autonomy through advanced machine learning techniques.