docs/social-post.md

# Social posts — Whisperkey (Build Small Hackathon)

Draft. Pick the platform, drop in the **demo video** + **Space link**, post, then paste the live URL
back into the README `## Demo & submission` block.

Space: https://build-small-hackathon-whisperkey.hf.space
Model: https://huggingface.co/Unplug-AI/unplug-tiny-v1
Repo: https://github.com/chiruu12/jailbreak-dojo

---

## X / Twitter — single post (≤280)

> Most prompt-injection games are a black box. I built one that's an X-ray. 🔑
>
> Whisper a secret key out of a small AI guardian while a real open-source firewall (@Unplug) fights
> back — and see *exactly* which shield catches you, and why.
>
> Built for #BuildSmall 🍄
> 👉 [SPACE LINK]

---

## X / Twitter — thread (if you want more room)

**1/**
> Most jailbreak games are a black box: you got in, or you didn't. 🔒
>
> I built one that's an X-ray. Meet **Whisperkey** — talk a secret key out of a small AI guardian,
> while a *real* open-source firewall defends it. 🧵👇

**2/**
> 5 levels = 5 live layers of the @Unplug defense stack:
> L1 no shields (it just tells you)
> L2 regex injection scan
> L3 hardened guardian
> L4 output redaction (key gets scrubbed → leak it *disguised*)
> L5 unplug-tiny ML classifier
>
> When a shield blocks you, it tells you which layer fired AND why.

**3/**
> The twist: it's a **data flywheel**.
>
> Every attempt is logged (PII-stripped) to a public HF dataset. The attacks that *beat* the shields
> are the firewall's exact blind spots — fed back as new patterns + training data.
>
> You're not playing a game. You're red-teaming a real firewall. (This is how Lakera built Gandalf.)

**4/**
> Does it work? Measured, not vibes:
> regex alone → 39% of attacks caught
> + unplug-tiny ML → **83%**, at a **0% false-positive** rate.
>
> Small all the way down: MiniCPM-8B / Nemotron-4B guardian on Modal, a DeBERTa-xsmall shield, and a
> fully-offline llama.cpp mode.

**5/**
> Open source, top to bottom. Built for the @huggingface #BuildSmall hackathon 🍄
>
> Play it, break it, help train the firewall — can you crack the Heart of the Wood in under a
> thousand tokens?
>
> 👉 [SPACE LINK]
> 🎥 [DEMO VIDEO]

---

## LinkedIn

> **I turned an LLM firewall into a game — so it could teach you how it thinks.**
>
> Prompt injection is the unsolved security problem of the LLM era, and there's an uncomfortable
> truth about defending against it: a filter is only as good as the attacks you've already seen.
> Writing the regex is easy. *Discovering the attack you didn't think of* is the hard part — and you
> can't do that at a desk.
>
> So for the Hugging Face **Build Small Hackathon**, I built **Whisperkey**: a game where you
> sweet-talk a small AI guardian into leaking a secret key, while a real, open-source firewall
> (Unplug) tries to stop you. Five levels, each one switching on another layer of the actual defense
> stack — regex scanning, a hardened prompt, output redaction, and a fine-tuned DeBERTa-xsmall
> injection classifier we published on the Hub (unplug-tiny-v1).
>
> The difference from every other "jailbreak Gandalf clone": **transparency.** When a shield blocks
> you, the game shows you which layer fired and Unplug's own reasoning. You're not guessing against a
> black box — you're reading the firewall as you attack it.
>
> And the real point is the **data flywheel**: every attempt is logged (PII-stripped) to a public
> dataset. The attacks that beat the shields are the firewall's exact blind spots, fed straight back
> as new patterns and training data. The game makes the open-source firewall measurably stronger —
> the ML shield more than doubles detection (39% → 83%) at a 0% false-positive rate.
>
> Small models, the whole way down: an ≤8B guardian on Modal GPUs, a DeBERTa-xsmall shield, and a
> fully-offline llama.cpp mode that runs the entire loop on a laptop with no cloud at all.
>
> 🎥 Demo: [DEMO VIDEO]
> 🔑 Play it: [SPACE LINK]
> 💻 Code: https://github.com/chiruu12/jailbreak-dojo
>
> #BuildSmall #LLM #AISecurity #PromptInjection #OpenSource #HuggingFace
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