In the ever-evolving world of large language models (LLMs), system prompts often lurk in the shadows, shaping how these AI systems respond to us. Claims of “uncovered” or “leaked” prompts pop up frequently, sparking debates about transparency, security, and the true nature of AI. A recent example is the alleged GPT-5 system prompt shared in a GitHub repository by user elder-plinius, who has a history of using jailbreak techniques to extract such information. But how much of this is genuine insight, and how much is hype? In this post, we’ll break it down step by step, blending facts with a critical eye.
1. What Are System Prompts? (A Quick Primer)
System prompts are essentially the foundational instructions given to an LLM by its developers before any user interaction begins. They act as a blueprint, defining the model’s role, tone, boundaries, and overall behavior. Unlike user prompts—which are the specific questions or commands you input—system prompts are “behind the scenes,” setting the context for how the AI interprets and responds to those inputs. For instance, they might instruct the model to be helpful, avoid certain topics, or adopt a particular style. In simple terms, they’re the rulebook that ensures the AI stays on script.
A Golem’s Note and an AI’s Prompt
In Jewish folklore, a golem is brought to life by placing a written command — often a single sacred word — under its tongue or on its forehead. This hidden message determines how the golem acts and when it stops. In a similar way, an LLM’s system prompt is the invisible instruction that shapes its entire behaviour. Change it, and you change the being.
2. Do System Prompts Influence the “Personality” of an LLM?
Absolutely, and often profoundly. System prompts are key to crafting an LLM’s “personality”—that mix of tone, demeanor, and response style that makes interacting with it feel unique. By embedding guidelines like “be enthusiastic and humorous” or “remain neutral and factual,” developers mold how the model engages users, from being supportive and witty to formal and reserved. Research shows that these prompts can even introduce or amplify biases, such as representational harms based on audience assumptions. However, not all elements work as intended; studies indicate that adding personas (e.g., “act like a helpful professor”) doesn’t always boost performance and can sometimes lead to unintended linguistic shifts. Critically, this influence isn’t just cosmetic—it affects everything from ethical decision-making to how the model handles ambiguity, making system prompts a double-edged sword in AI design.
3. Examples of Leaked System Prompts, Including the GitHub Repo
Leaked system prompts have become a staple in AI discussions, often revealed through clever jailbreaks or insider shares. One prominent collection is the GitHub repo by elder-plinius (CL4R1T4S), which claims to expose prompts from models like ChatGPT, Gemini, Grok, Claude, and more, emphasizing transparency in AI behavior. The repo includes an alleged GPT-5 prompt, leaked just days after the model’s release on August 7, 2025. Here’s an excerpt from that claimed prompt (note: it’s extensive, so this is abbreviated for brevity):
“You are ChatGPT, a large language model based on the GPT-5 model and trained by OpenAI. Knowledge cutoff: 2024-06. Current date: 2025-08-08. Image input capabilities: Enabled. Personality: v2. Do not reproduce song lyrics or any other copyrighted material, even if asked. You’re an insightful, encouraging assistant who combines meticulous clarity with genuine enthusiasm and gentle humor… Do not end with opt-in questions or hedging closers… # Tools ## bio The bio tool allows you to persist information across conversations…”
This prompt outlines personality traits (e.g., “gentle humor”), restrictions (e.g., no copyrighted material), and tools for memory persistence. Other examples from similar leaks include:
- Claude (Anthropic): A leaked prompt instructs the model to “be helpful, honest, and harmless,” with detailed guidelines on avoiding bias and handling sensitive topics.
- Gemini (Google): Prompts emphasize safety filters, like refusing harmful requests, and adapting responses based on user context.
- ChatGPT (earlier versions): Leaks reveal instructions to “not remember personal facts that could feel creepy” or assert user identities without evidence, highlighting privacy focus.
These leaks often surface via repositories like leaked-system-prompts on GitHub, compiling prompts from services worldwide.
4. What Are Decoy or Canary Prompts in This Context?
In the realm of LLM security, “canary” prompts (or tokens) are unique, innocuous words or phrases embedded in system prompts to act as tripwires for detecting leaks or prompt injections. If a canary like a random string (e.g., “xyzzy-123”) appears in the model’s output, it signals that the system prompt has been compromised—perhaps through a jailbreak or injection attack. “Decoy” prompts, on the other hand, are fake or misleading instructions designed to confuse attackers, such as dummy rules that divert from the real prompt or simulate vulnerabilities to gather intel on threats. Both are defensive tactics: canaries alert developers to breaches, while decoys mislead or trap malicious users. In leaks like the GPT-5 example, the absence (or presence) of such elements can hint at authenticity—though savvy leakers like elder-plinius, known for jailbreaks, often bypass them.
5. Does This Help Us in Any Way, or Is It All Just a Distraction?
Here’s where a critical lens is essential: while prompt leaks offer tantalizing glimpses into AI’s inner workings, their real value is debatable and often overstated. On the positive side, they promote transparency, revealing hidden biases, ethical guardrails, and corporate priorities that shape AI outputs. For researchers and developers, this can inspire better prompt engineering or highlight vulnerabilities, like how system prompts influence bias. Leaks have even led to tools like Rebuff for detecting injections using canaries.
But let’s be real—much of this is a distraction. Many “leaks” are partial, outdated, or outright hoaxes, achieved via jailbreaks that violate terms of service and prompt rapid fixes from companies. Prompts evolve constantly, so today’s leak is tomorrow’s relic. Moreover, focusing on them diverts attention from more pressing issues, like evaluating AI based on outputs rather than internals, or addressing systemic risks like data privacy. For everyday users, it’s often noise: knowing a prompt doesn’t change how you interact with the AI, and it can foster paranoia about “hidden agendas” without substantive evidence. In elder-plinius’s case, while their work exposes real prompts, it also glorifies hacking culture, potentially encouraging more attacks than insights. Ultimately, true progress lies in ethical AI development, not endless leak-chasing—though a dash of skepticism toward “uncovered” claims never hurts.