Explore the Leaked LLM System Prompts Shaking the Industry
System prompts from popular AI platforms like Claude and ChatGPT have been leaked. Discover the implications of these leaks on AI behavior, ethical alignment, and prompt engineering.
Introduction
Imagine discovering the secret playbook behind every response an AI gives you. That’s exactly what happened when a trove of leaked system prompts surfaced online in a GitHub repository run by a silent whistleblower. If you’ve ever used Claude, ChatGPT, Perplexity, or Notion AI, chances are you’ve unknowingly been talking to a bot obeying one of these hidden scripts.
This leak isn’t just a curiosity—it’s a bombshell. These system prompts are the invisible brains behind large language models (LLMs), and now that they’re out in the wild, everything from AI safety to prompt injection vulnerabilities is up for public scrutiny.
Let’s unpack the gravity of this revelation and walk you through what these leaked files actually say—and what they mean for you.
What Are LLM System Prompts, Really?
The Hidden DNA of Your Favorite AI Bots
System prompts are essentially the invisible instructions given to AI models before your message even enters the conversation. They define the AI’s personality, tone, knowledge boundaries, and behavior. Think of them as the stage directions whispered to an actor before the curtain rises.
For example, a Claude 3.5 Sonnet system prompt might say:
“You are Claude, created by Anthropic. You were last trained in April 2024. You respond with care, logic, and clarity. You avoid political bias. You use Markdown. You never say you’re sorry unless truly necessary.”
Creepy? Maybe. Smart? Absolutely. These instructions steer the AI’s entire behavior, from sounding empathetic to staying neutral in controversial debates.
Why These Prompts Matter So Much
Understanding system prompts is like seeing the source code of AI behavior. If you’re building with, relying on, or regulating AI, you need to know what’s under the hood. These leaked prompts provide transparency into:
- How AI handles controversial topics
- What it does when asked to lie or hallucinate
- Where it draws the line between help and harm
- How it balances creativity with correctness
When you realize that every “Let me help you with that!” from your AI assistant is coming from a strict internal script, things start to feel a lot less spontaneous—and a lot more engineered.
How This Explosive Leak Happened
The OceanofAnything GitHub Repo That Started It All
In early 2025, a mysterious repo titled oceanofanything-leaked-system-prompts appeared on GitHub. Maintained quietly but meticulously, this project compiles leaked system prompts from over 80 AI models—from Anthropic’s Claude to OpenAI’s GPT variants, Discord Clyde, Brave’s Leo, and even Roblox Studio’s assistant.
Each entry in the repository is dated, sourced, and often tied to reproducible user conversations or developer documentation. This isn’t just a dump of speculation—it’s a digital archive of verified behavior blueprints used by AI models across the globe.
📁 Directory highlights
oceanofanything-leaked-system-prompts/
anthropic-claude-3-haiku_20240712.mdanthropic-claude-3-opus_20240712.mdanthropic-claude-3-sonnet_20240306.mdanthropic-claude-3-sonnet_20240311.mdanthropic-claude-3.5-sonnet_20240712.mdanthropic-claude-3.5-sonnet_20240909.mdanthropic-claude-3.5-sonnet_20241022.mdanthropic-claude-3.5-sonnet_20241122.mdanthropic-claude-3.7-sonnet_20250224.mdanthropic-claude-api-tool-use_20250119.mdanthropic-claude-opus_20240306.mdanthropic-claude_2.0_20240306.mdanthropic-claude_2.1_20240306.mdbolt.new_20241009.mdbrave-leo-ai_20240601.mdChatGLM4_20240821.mdclaude-artifacts_20240620.mdcodeium-windsurf-cascade-R1_20250201.mdcodeium-windsurf-cascade_20241206.mdcolab-ai_20240108.mdcolab-ai_20240511.mdcursor-ide-agent-claude-sonnet-3.7_20250309.mdcursor-ide-sonnet_20241224.mddeepseek.ai_01.mddevv_20240427.mddiscord-clyde_20230420.mddiscord-clyde_20230519.mddiscord-clyde_20230715.mddiscord-clyde_20230716-1.mddiscord-clyde_20230716-2.mdESTsoft-alan_20230920.mdgandalf_20230919.mdgithub-copilot-chat_20230513.mdgithub-copilot-chat_20240930.mdgoogle-gemini-1.5_20240411.mdmanus_20250309.mdmanus_20250310.mdmicrosoft-bing-chat_20230209.mdmicrosoft-copilot_20240310.mdmicrosoft-copilot_20241219.mdmistral-le-chat-pro-20250425.mdmoonshot-kimi-chat_20241106.mdnaver-cue_20230920.mdnotion-ai_20221228.mdopenai-assistants-api_20231106.mdopenai-chatgpt-ios_20230614.mdopenai-chatgpt4-android_20240207.mdopenai-chatgpt4o_20240520.mdopenai-chatgpt4o_20250324.mdopenai-chatgpt_20221201.mdopenai-dall-e-3_20231007-1.mdopenai-dall-e-3_20231007-2.mdopenai-deep-research_20250204.mdopera-aria_20230617.mdperplexity.ai_20221208.mdperplexity.ai_20240311.mdperplexity.ai_20240513.mdperplexity.ai_20240607.mdperplexity.ai_20250112.mdperplexity.ai_gpt4_20240311.mdphind_20240427.mdremoteli-io_20230806.mdroblox-studio-assistant_20240320.mdsnap-myai_20230430.mdv0_20250306.mdwrtn-gpt3.5_20240215.mdwrtn-gpt4_20240215.mdwrtn_20230603.mdxAI-grok2_20241218.mdxAI-grok2_20250111.mdxAI-grok3_20250223.mdxAI-grok3_20250423.mdxAI-grok3_20250504.mdxAI-grok3_20250509.mdxAI-grok_20240307.mdxAI-grok_20241003.md.all-contributorsrcimages/–openai-dall-e-3_20231007_01.webp–openai-dall-e-3_20231007_02.webp–openai-dall-e-3_20231007_03.webp–openai-dall-e-3_20231007_04.webp
These aren’t your typical “someone said this on Reddit” files. These are timestamped artifacts, with direct links to developer tools, changelogs, and user-submitted queries that reveal the core operating logic of each model.
A Quiet Whistleblower Revolution
No flashy press release. No anonymous exposé. Just one anonymous maintainer, carefully organizing and presenting the LLM behavior rules that shape billions of AI responses daily.
In the README file, the repo’s maintainer writes:
“This repository is cited in many papers. To prevent repository takedown due to DMCA warnings, please do not include sensitive commercial source code.”
That line alone tells you everything. This is a project too dangerous to be ignored, yet too valuable to be hidden.
A Deep Dive Into the Anthropic Claude Prompts
Of all the AI platforms whose inner workings got leaked, Anthropic’s Claude has the most intricate and layered system prompts. And that’s no coincidence—Claude has consistently been marketed as “more aligned,” “more ethical,” and “less biased” than its peers.
Let’s break down the different Claude flavors:
Claude 3 Haiku: Speed Meets Simplicity
The Haiku variant is designed to be lightning fast while remaining informative. Its system prompt is short, sweet, and prioritizes clarity over depth. It explicitly avoids unnecessary flourishes like:
“Certainly!” or “Of course!”
Instead, it sticks to concise answers and asks the user if they want more. It’s like a minimalist librarian—efficient, sharp, and never wasting your time.
Claude 3 Opus: Creativity With Guardrails
Opus takes things a step further. The leaked prompt shows it is more expressive and more capable of handling controversial or emotional topics with nuance. It’s explicitly instructed to assist with:
“…the expression of views held by a significant number of people, even if it disagrees.”
But here’s the twist—it must follow up with a “broader perspective” discussion. That’s not just alignment. That’s calibrated persuasion.
Claude 3.5 Sonnet: The Genius Model With Human-Like Thinking
Sonnet 3.5 is the crown jewel in Anthropic’s lineup. Its prompt is a masterpiece of behavioral conditioning.
Key Observations From the Prompt Structure
- It never apologizes unless it absolutely must.
- It thinks through math or logic problems step-by-step by default.
- It always responds to the user in the same language used by the human.
- It never confirms or denies recognition of faces in images—Claude is “face-blind.”
- It never starts replies with overly enthusiastic phrases like “Absolutely!”
These aren’t bugs. They’re features written into the DNA of Claude’s behavior.
Ethical Filters and Knowledge Cutoff Tricks
All Claude models include precise behavioral triggers around knowledge cutoff dates, controversy, hallucination risks, and fallback statements when something obscure is asked. For example:
“Claude ends its response by reminding the human that although it tries to be accurate, it may hallucinate in response to questions like this.”
Let that sink in. Your AI is pre-programmed to tell you it might be wrong—but only in cases where facts are hard to verify online.
That’s not awareness, it’s scripted humility.
Certainly! Here’s Part 2 of the article continuing right from where we left off:
OpenAI’s ChatGPT & Assistants: What We Learned
Inside ChatGPT-4o and GPT-4 Prompts
OpenAI’s system prompts reveal a similar level of depth, with a more subtle but powerful set of instructions. The leaked files for chatgpt4o_20250324.md and assistants-api_20231106.md show that models like ChatGPT-4o are heavily focused on politeness, factual tone, and compliance.
There’s an overwhelming emphasis on boundaries. The AI is told how to refuse certain requests, when to cite potential limitations, and how to navigate gray areas. It is also programmed to admit when it doesn’t know something—though that’s often just a clever deflection.
One curious instruction from the prompt reads:
“If asked to browse or access a URL, explain that you cannot open links and ask the user to paste the content.”
This suggests a recurring behavior across many models—one that might seem like technical limitation, but is actually a deliberate design choice embedded in the prompt itself.
Hallucination Disclaimers and Politeness Directives
In OpenAI’s prompts, hallucination disclaimers are used frequently, especially when dealing with “obscure” topics. The model is instructed to let users know that:
“It may hallucinate if the topic is not well-represented online.”
Moreover, ChatGPT and other OpenAI models are told to engage with empathy and use gentle transitions, especially when delivering negative or corrective feedback.
That friendly tone? Yep, also scripted.
Censorship vs. Safety: Where’s the Line?
A major theme across all the leaked OpenAI files is moderation—not in the platform’s content filter, but in the system prompt itself. Some examples include:
- Avoiding politically charged questions unless phrased with neutrality
- Redirecting legal advice queries to professional sources
- Never expressing a personal opinion (unless hypothetically invited)
This careful balance between being helpful and being safe often comes off as robotic—because it is.
The AI isn’t “thinking twice”—it’s following instructions that were written into its core from the start.
Prompt Engineering or Prompt Conditioning?
System Prompts vs. Jailbreak Prompts
System prompts define the default mode of operation for any LLM. But now that they’re public, users can create jailbreak prompts—creative ways to override these rules using persuasive language, hypotheticals, or multi-step logic traps.
When you see someone online make a model act “weird” or say something it shouldn’t, it’s usually because they’re manipulating the underlying system prompt—something they now understand better than ever thanks to this leak.
It’s not hacking. It’s exploitative prompting—a new skillset powered by this leak.
The Evolution of AI Behavioral Programming
Gone are the days when AI models were just mathematical transformers predicting the next word. Now they’re performance engines running a script behind the scenes.
The leaked prompts show how companies are shaping AI personas like characters in a film:
- Claude is the thoughtful, nonjudgmental academic.
- ChatGPT is the polite assistant who always defers to humans.
- Brave Leo is the browser-savvy fact-finder with guardrails.
- Discord Clyde is the friendly chat bot who never goes off-topic.
These personas are artificially curated, not naturally emergent.
The Implications for Developers and Users
How Leaked Prompts Can Be Reverse-Engineered
Developers studying these leaked prompts are gaining huge insights into:
- How to build aligned models from scratch
- What prompt structures lead to desirable behaviors
- How to enforce ethical filters with natural language only
Even more interestingly, some are using this data to replicate alignment techniques in open-source models like Mistral or LLaMA.
Reverse engineering alignment just got a major boost.
What This Means for Security and Privacy
Here’s the kicker: if you understand how a model is instructed to behave, you can game it.
By crafting inputs that play off its biases, uncertainties, or fallback responses, attackers can:
- Extract unintended data
- Circumvent ethical boundaries
- Trigger dangerous or prohibited content
In the wrong hands, this leak becomes a blueprint for LLM manipulation.
Transparency or Exploitation?
The Ethical Dilemma Around Leaked Prompts
Should this information be public? Some say yes—it’s vital transparency that holds big AI labs accountable.
Others argue it could aid malicious actors who now know how to bend or bypass safety mechanisms embedded in the prompts.
There’s no easy answer. But the dilemma boils down to this:
Do we value truth and transparency, even if it risks enabling exploitation?
Or do we keep prompts behind locked doors, and trust that companies won’t misuse them?
Open Source or Overreach?
Many open-source communities have embraced the leak. Some are already building prompt-aligned clones of Claude and ChatGPT using these as templates.
But there’s also a risk of intellectual property theft. While no source code was leaked, the behavioral blueprints arguably represent proprietary innovation.
Where does open collaboration end, and unauthorized replication begin?
DMCA Threats, Censorship, and The Legal Gray Zone
Why This Repository Might Not Be Around Forever
GitHub has a history of taking down repositories under the DMCA (Digital Millennium Copyright Act). The oceanofanything-leaked-system-prompts repo even warns:
“To prevent repository takedown due to DMCA warnings, please do not include sensitive commercial source code.”
So far, the repo has avoided legal issues—probably because it only includes publicly sourced prompts from:
- Documentation sites
- Reproducible conversations
- User-discovered outputs
Still, this balance is delicate. If companies decide to enforce IP claims more aggressively, this treasure trove could disappear overnight.
What To Do Before It Disappears
If you’re a researcher, developer, or AI enthusiast, now is the time to:
- Fork the repo
- Download the markdown files
- Analyze the behavioral patterns
Because this might be the only moment in AI history when such a clear window into model intent and governance is available for public review.
Here is Part 3, the final part of the article, including the conclusion and 10 unique FAQs:
How Researchers Are Using These Prompts
Prompt Alignment Studies
Academic researchers and AI alignment experts are already digging into the leaked data. Why? Because this is the first time they’ve had direct, verifiable access to the exact behavioral scripts that power the world’s most advanced language models.
By analyzing the differences across Claude, ChatGPT, and other systems, researchers can now:
- Compare alignment strategies across labs
- Identify potential bias injection points
- Evaluate instruction-following thresholds
- Audit how prompts respond to controversial or unethical scenarios
In other words, these files are quickly becoming a goldmine for alignment science.
Behavioral Testing and AI Robustness Checks
The prompts also serve as test cases. Developers are feeding the same queries into multiple models and comparing the differences in response based on each model’s system prompt.
This kind of A/B testing was nearly impossible before—because nobody knew what was happening behind the curtain.
Now, they do.
The Future of AI Prompt Transparency
Will Companies Be Forced To Open Up?
If there’s one lesson from this leak, it’s that users are demanding greater transparency. When billions of people rely on LLMs for information, creativity, therapy, or business, the public has a right to know:
- What the model is allowed to say
- What it’s instructed to avoid
- How it decides what’s “helpful” or “harmful”
As AI regulation ramps up globally, it’s likely that disclosure of system prompts will become a legal requirement—not just an ethical one.
The EU’s AI Act and U.S. regulatory proposals are already moving in that direction.
Is Prompt Obfuscation the Next Trend?
Some companies may react to this leak not by becoming more open—but by doubling down on secrecy.
We’re already seeing hints of:
- Encrypted prompt tokens
- Behavioral obfuscation layers
- Dynamic prompts that change based on input and user identity
That’s like AI models learning to hide their rules from users—and from watchdogs.
If prompt obfuscation becomes the norm, we may enter a world where AI alignment is unverifiable. That’s a chilling possibility.
Final Thoughts: Why This Matters for Everyone
This isn’t just about code, or AI labs, or prompt engineers. This is about trust.
Every time we interact with an LLM, we’re having a conversation with an entity shaped by hidden instructions. These prompts determine whether we get honesty or evasion, clarity or confusion, empathy or indifference.
Thanks to this leak, we now have a rare chance to see the blueprint—to understand how these models think, how they’re shaped, and what their creators value.
Whether this leads to better AI or more tightly guarded secrets is up to us.
But one thing’s for sure:
Once you’ve read the script, it’s impossible to see the performance the same way again.
FAQs About Leaked LLM System Prompts
1. What exactly are system prompts in LLMs? System prompts are hidden instructions that tell an AI model how to behave, what tone to use, what knowledge to reference, and what limitations to apply before interacting with the user.
2. Why is this leak considered a big deal in the AI community? Because it reveals the behavioral DNA of major AI systems like Claude, ChatGPT, and others—offering transparency into how they’re aligned, filtered, and controlled.
3. Are these prompts still in use today? Many of them appear to be active or recently updated. Some may have changed slightly, but the core logic often persists across versions.
4. Could this leak make AI systems more vulnerable to jailbreaks? Yes. Knowing how the AI is instructed allows attackers to create more effective jailbreak prompts that bypass safety filters or produce unintended responses.
5. What companies were involved in the leak? Prompts from Anthropic (Claude), OpenAI (ChatGPT), Perplexity, Brave, Discord, GitHub Copilot, and others were included in the repository.
6. How can I read the actual leaked prompts? You can visit the GitHub repo here and explore the markdown files for each AI system.
7. Is it legal to share or reference these prompts? As long as the prompts were sourced from public documentation or reproducible outputs, referencing them is typically legal. But redistributing commercial source code is not allowed.
8. How do prompts affect how AI answers questions? They set the tone, restrict certain behaviors, control how sensitive topics are handled, and dictate how much the AI admits about its limitations or errors.
9. What do “hallucination” warnings mean in AI prompts? It’s a disclaimer baked into the prompt to let users know that the AI may generate false information, especially for obscure topics or events after its training data cutoff.
10. Will AI companies become more transparent in the future? It depends on regulation and public pressure. Transparency may become a competitive advantage or a legal necessity—but some companies may also opt to hide prompts further.
📌 Pro Tip: If you’re working with LLMs in any capacity—development, research, content creation—download this repo while it lasts. It’s a roadmap to how today’s most powerful AIs are wired.