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| title: README | |
| emoji: 🛡️ | |
| colorFrom: indigo | |
| colorTo: gray | |
| sdk: static | |
| pinned: false | |
| # 🛡️ Patronus Protect | |
| **An on-device AI firewall.** It watches the AI traffic going in and out of your apps — | |
| browsers, IDEs, native apps, MCP servers — and blocks what shouldn't get through: prompt | |
| injections, leaked secrets, shadow AI. Everything runs locally, nothing is sent to a cloud, | |
| and it works with any provider (Claude, OpenAI, Gemini, Copilot, Ollama, …). | |
| ## Patronus Ark | |
| **Patronus Ark** is the scanning engine underneath the app: a Rust core with Python | |
| bindings that scans in three escalating layers, so most traffic is resolved before a | |
| transformer ever runs. | |
| | Layer | What runs | Cost | | |
| | --- | --- | --- | | |
| | **L1** | Native rule-based detectors | microseconds | | |
| | **L2** | Small classifiers sharing one encoder | milliseconds | | |
| | **L3** | Full quantized transformers, lazily loaded | only for the uncertain rest | | |
| Across those layers it catches prompt injection and jailbreaks, PII, secrets and DLP leaks, | |
| sensitive documents, risky agentic tool use and MCP policy violations, plus routing and | |
| threat classification — all on the endpoint. | |
| Ark will be open source (dual license available). It is **not public yet** | |
| — the repository link will appear here at launch. | |
| ## The models 🤗 | |
| Our detectors, trained for the firewall and shared openly — use them, fine-tune them, | |
| benchmark against them, and tell us where they fall short: | |
| - 🐺 **Wolf Defender** — prompt-injection detection and threat classification (EN/DE) | |
| - **Orca Sonar** — document classification for DLP and sensitive-document routing | |
| - 🦁 **Lion Warden** — one unified seven-head AI-security model | |
| - 🐕 **Husky** (Sight / Paw / Nose) — agentic tool use: tool type, operation, data-flow risk | |
| - 🐆 **Panther Read** — user-intent and request routing | |
| - 🦈 **Shark Scent** — PII detection | |
| - **GLiNER Edge** — quantized zero-shot entity extraction (upstream GLiNER, Apache-2.0) | |
| Deliberately small: ModernBERT under the hood, ONNX-quantized (INT8/INT4), a few hundred | |
| MB of RAM — they run happily on a laptop instead of a GPU rack. | |
| The app, with downloads for macOS and Windows, lives at | |
| [patronus.studio](https://patronus.studio). Come say hi. | |