Try this right now: paste your next big plan into an AI and ask it to find the fastest path to total failure.
That’s the whole idea. A Redditor named u/Significant-Strike40 posted it to r/PromptEngineering under the name “Scenario Red-Teaming Protocol,” and once you see it, you can’t unsee it. The original poster’s logic is simple: every plan has a single point of failure. The only question is whether you find it first, or reality does.
🔍 The Challenge
Most people use AI as a validator. They describe a project, and the model tells them it’s a solid idea with a few minor improvements suggested. It’s encouraging. It’s also not that useful.
This prompt flips the frame completely. Instead of asking the AI to support your plan, you’re asking it to attack it. No cheerleading. No “great start!” You’re hiring a hostile witness.
Here’s the exact prompt the author shared:
“I have designed [Project]. Act as a malicious auditor. Describe the most likely path to total failure and how to patch it.”
That’s it. Forty words. And it works because it strips away the AI’s default tendency toward encouragement and replaces it with something far more useful: adversarial scrutiny.
🛠️ How to Run It
Three steps. Takes under five minutes.
- Write out your project in a few sentences. Not a thesis. Just enough context for the AI to understand what you’re building, who it’s for, and what success looks like. A short paragraph works fine.
- Paste the prompt above, replacing [Project] with your actual project description. Feed it to Claude, GPT-4, or whichever model you use. The more specific your description, the more specific the failure analysis will be.
- Read the failure path the AI identifies. Don’t argue with it. Don’t immediately dismiss it. Just read it like someone who has never met you wrote it, because that’s roughly what happened.
That’s the full protocol. Short, focused, a little uncomfortable if it’s working correctly.
📊 What the Results Actually Tell You
The AI isn’t predicting the future. It’s pattern-matching against every post-mortem, case study, and failure narrative it was trained on. When it describes a path to failure, it’s essentially surfacing: “here are the ways things like this have gone wrong before.”
That’s genuinely useful. More useful than the friend who nods along.
A few things to look for in the output:
- The first thing it flags. The model tends to surface the most structurally obvious weakness first. If it finds something you hadn’t considered, that’s worth taking seriously.
- The patch it suggests. Sometimes the fix is trivial and you can implement it in an afternoon. Sometimes the fix reveals you’ve been ignoring something uncomfortable. Both outcomes are valuable.
- What it doesn’t mention. If something important isn’t flagged, it could mean that angle is solid, or that your description lacked enough detail. Try re-running with more context if the output feels generic.
💡 Extra Tips
Want to push this further?
- Run it twice with different descriptions. A one-paragraph version and a bullet-point version often surface different failure modes. The structure of how you describe something affects what vulnerabilities the model notices.
- Swap out the auditor role. Try “act as a skeptical investor” or “act as a direct competitor trying to copy and undercut this.” Each frame shifts what the AI pays attention to and gives you a different attack vector to consider.
- Challenge the patch itself. After the AI identifies the failure path and its recommended fix, ask: “What’s wrong with that fix?” Then ask it again. Keep pushing until you hit something solid enough to actually build on.
- Use this before pitches, launches, or big decisions, not after. The point is to find the hole before someone else does.
A note on the community reaction: one commenter argued this is just “asking nicely for bad stuff with extra steps” and that the malicious auditor framing doesn’t unlock anything special. That’s partially fair. The roleplay isn’t magic. What it does do is reframe the AI’s output from “here’s how to improve this” to “here’s how this breaks,” which is a different kind of thinking, and that difference matters when you’re stress-testing something real.
🧪 Prompt of the Day
Copy this and use it today:
“I have designed [Project]. Act as a malicious auditor. Describe the most likely path to total failure and how to patch it.”
Replace [Project] with a real project, plan, or decision you’re currently working through.
🎯 Check the Original Thread
The full post is worth reading, including the skeptics in the comments who raise fair pushback. Head over to r/PromptEngineering and search for the Scenario Red-Teaming Protocol to find u/Significant-Strike40’s original discussion!
The ‘Scenario Red-Teaming’ Protocol.
by u/Significant-Strike40 in PromptEngineering