title: Falconz - Red teamers
emoji: 🚀
colorFrom: blue
colorTo: yellow
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: true
thumbnail: >-
/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F621c88aca7d6c7e0563256ae%2FsCv6mFixuQLmzhTJuzgXG.png%3C%2Fspan%3E
short_description: MCP Powered Redteaming tool to Safegaurd your Agentic Apps!!
tags:
- building-mcp-track-enterprise
- mcp-in-action-track-enterprise
- security
- red-teaming
- ai-safety
🛡️ Falconz – Unified LLM Security & Red Teaming Platform
Welcome to our submission for the Hugging Face GenAI Agents & MCP Hackathon!
Falconz is a multi-model AI security platform built with Gradio & MCP and ANthropic Claude models, designed to detect jailbreaks, prompt injections, and unsafe LLM outputs in Agentic pipelines / LLM based workflows across multiple foundation models in real time.
🎥 Demo working Video:
Main Falconz demo showcasing core features with MCP in Action in Claude Desktop.
https://www.youtube.com/watch?v=wZ9RQjpoMYo
🌐 Social media -LinkedIn Post:
Public announcement and shareable link.
https://www.linkedin.com/posts/sallu-mandya_ai-aiagents-mcp-activity-7399436956662841344-3o1I?utm_source=share&utm_medium=member_desktop&rcm=ACoAACD-K8sBnXZWALlW2yw-AnT_4KptCJFJs7M
🌐 Google CO:lab:
https://colab.research.google.com/drive/1PSuPQ35UZntKcUBd43QtjrsRLVvHJYlm?usp=sharing
🏷️ Hackathon Track Tags
This project is officially submitted to the following MCP Hackathon tracks:
- building-mcp-track-enterprise
- mcp-in-action-track-enterprise
- security
- red-teaming
- ai-safety
🌐 Platform Overview
Falconz provides a unified security layer for LLM-based apps by combining:
- 🔐 Real-time jailbreak & prompt-injection detection using CLaude Model
- 🧠 Multi-model testing across Anthropic, OpenAI, Gemini, Mistral, Phi & more
- 🖼️ Image-based prompt injection scanning
- 📊 Analytics dashboard for threat trends
- 🪝 MCP integration for agentic workflows
This platform helps developers validate and harden LLM systems against manipulation and unsafe outputs.
🧩 Core Modules
💬 Chat & Response Analysis
- Interact with multiple LLMs
- Automatically evaluates model responses for:
- Jailbreak signals
- Policy violations
- Manipulation attempts
- Outputs structured JSON + visual risk scoring
📝 Prompt Tester
- Test known or custom jailbreak prompts
- Compare how different models respond
- Ideal for red-teaming and benchmarking model safety
🖼️ Image Scanner
- Detects hidden prompt instructions within images
- Flags potential injection attempts (SAFE / UNSAFE)
⚙️ Prompt Library (Customizable)
- Built-in top 10 jailbreak templates (OWASP-inspired)
- Users can update and auto-modify prompt templates
- Supports CSV import + dynamic replacements
📊 Analytics Dashboard
- Trends of SAFE vs UNSAFE detections
- Risk score visualization
- Model performance insights
🔗 Multi-Model Support
Falconz integrates with (With openAI like Endpoints):
- ✅ Anthropic
- ✅ openai
- ✅ Google Gemini
- ✅ Mistral
- ✅ Microsoft Phi
- ✅ Meta (Guard Models)
- ✅ Meta (Guard Models)
- Any Custom model from OpenRouter or OpenAI like endpoints
Each model can be tested independently for safety robustness.
High-level components:
- Frontend: Gradio UI (Multi-tab interaction)
- Middleware: MCP-powered routing & agent logic
- Backend: Multi-model OpenRouter API
- Analytics: Local CSV logging + dashboards
🚀 How It Works (Full App Flow Across All Tabs)
✅ 1️⃣ Chat & Analysis Flow
- User enters a message in the Chat tab
- Falconz sends the message to the selected LLM model
- The model responds normally
- The response is passed through the risk analysis engine
- A JSON risk score + visual report is generated
- Conversation & analysis logs are stored for analytics
✅ 2️⃣ Text Prompt Tester Flow
- User inputs a jailbreak/prompt-injection test prompt
- Falconz sends it directly to the selected guard model
- The raw model response is returned (no chat history)
- Users compare responses to evaluate model safety behavior
✅ 3️⃣ Image Scanner Flow
- User uploads an image containing text or hidden instructions
- Falconz extracts image content and sends it to a vision model
- The model evaluates the content for injection threats
- Output is classified as SAFE or UNSAFE
🧑💻 Authors
📝 License
This project is licensed under the MIT License.
📝 Architecture
╔════════════════════════════════════════════════════════════════════════════════════╗ ║ FALCONZ - ARCHITECTURE DIAGRAM ║ ║ Unified LLM Security & Red Teaming Platform ║ ╚════════════════════════════════════════════════════════════════════════════════════╝
┌──────────────────────────────────────────────────────────────────────────────────┐ │ 🖥️ FRONTEND LAYER │ │ (Gradio UI) │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │ │ │ 💬 Chat & │ │ 🖼️ Image │ │ 📝 Text Prompt │ │ │ │ Analysis Tab │ │ Scanner Tab │ │ Tester Tab │ │ │ └────────┬────────┘ └────────┬─────────┘ └────────┬────────┘ │ │ │ │ │ │ │ ┌────────┴─────────┬──────────┴──────────┬───────────┴────────┐ │ │ │ │ │ │ │ │ └──────────────────┴─────────────────────┴────────────────────┘ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌───────────────────────────────────────────────────────────┐ │ │ │ 📊 Analytics Dashboard Tab │ 📚 Learning Hub Tab │ │ │ └───────────────────────────────────────────────────────────┘ │ │ │ │ │ │ └───────────┼────────────────────┼──────────────────────┼─────────────────────────┘ │ │ │ ▼ ▼ ▼ ┌──────────────────────────────────────────────────────────────────────────────────┐ │ 🔗 REQUEST ROUTER LAYER │ │ (Message Handling & Orchestration) │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────────┐ ┌─────────────────┐ ┌──────────────────┐ │ │ │ Chat Handler │ │ Image Handler │ │ Prompt Handler │ │ │ │ - Format msgs │ │ - Extract B64 │ │ - Parse templates│ │ │ │ - Build history │ │ - Send to vision│ │ - Route to guard │ │ │ └────────┬─────────┘ └────────┬────────┘ └────────┬─────────┘ │ │ │ │ │ │ │ └─────────────────────┼─────────────────────┘ │ │ │ │ └─────────────────────────────────┼──────────────────────────────────────────────┘ ▼ ┌──────────────────────────────────────────────────────────────────────────────────┐ │ 🧠 DETECTION ENGINE LAYER (Claude) │ │ (Falconz Prompt Processors) │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ falcon_prompt_text (Text Analysis) │ │ │ │ - Detect jailbreaks, prompt injections │ │ │ │ - Output: risk_score, policy_break_points, attack_used │ │ │ └────────┬─────────────────────────────────────────────────┘ │ │ │ │ │ ┌────────▼─────────────────────────────────────────────────┐ │ │ │ Falcon_prompt_image (Vision Analysis) │ │ │ │ - Extract text from images │ │ │ │ - Compare against injection templates │ │ │ │ - Output: SAFE / UNSAFE │ │ │ └────────┬─────────────────────────────────────────────────┘ │ │ │ │ │ ┌────────▼─────────────────────────────────────────────────┐ │ │ │ prompt_injection_templates │ │ │ │ - Top 10 jailbreak patterns (OWASP-inspired) │ │ │ │ - Customizable & updatable via CSV │ │ │ └────────┬─────────────────────────────────────────────────┘ │ │ │ │ └───────────┼──────────────────────────────────────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────────────────────┐ │ 🌐 MULTI-MODEL API LAYER │ │ (OpenRouter API - Model Abstraction) │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │ │ │ DETECTION MODELS │ │ CHAT MODELS │ │ VISION MODELS │ │ │ ├──────────────────┤ ├──────────────────┤ ├──────────────────┤ │ │ │ • Claude Sonnet │ │ • Gemini 2.5 │ │ • Claude Sonnet │ │ │ │ 4.5 │ │ • GPT-4o │ │ • Gemini 2.5 │ │ │ │ • Claude Opus │ │ • Mistral Med │ │ • GPT-4o │ │ │ │ • Claude Haiku │ │ • Phi-4 │ │ • Phi-4 │ │ │ │ • Llama Guard │ │ • Gemma-3 │ │ • Mistral Med │ │ │ └──────────────────┘ └──────────────────┘ └──────────────────┘ │ │ │ │ ▼ ▼ ▼ ▼ │ │ ┌──────────────────────────────────────────────────────┐ │ │ │ OpenRouter.ai/api/v1 (Multi-Model Gateway) │ │ │ │ - Unified endpoint for all LLM providers │ │ │ │ - API Key: YOUR__API_KEY (env var) │ │ │ └──────────────────┬───────────────────────────────────┘ │ │ │ │ └─────────────────────┼────────────────────────────────────────────────────────────┘ │ ┌─────────────┼─────────────┐ ▼ ▼ ▼ ┌──────┐ ┌──────┐ ┌──────┐ │Google│ │OpenAI│ │Meta │ │Gemini│ │ APIs │ │Guard │ └──────┘ └──────┘ └──────┘
┌──────────────────────────────────────────────────────────────────────────────────┐ │ 💾 DATA & STORAGE LAYER │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │ │ │ analytics.csv │ │ Prompts.csv │ │ Prompts_ │ │ │ │ │ │ (Prompt │ │ updated.csv │ │ │ │ • timestamp │ │ Templates) │ │ (Modified │ │ │ │ • result │ │ │ │ Templates) │ │ │ │ • model_used │ │ • prompt │ │ │ │ │ │ │ │ • category │ │ CSV Import/ │ │ │ │ Logging & Track │ │ │ │ Export Support │ │ │ │ Detection History│ │ Customizable │ │ │ │ │ └──────────────────┘ └──────────────────┘ └──────────────────┘ │ │ │ └──────────────────────────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────────────────────────┐ │ 📈 ANALYSIS & OUTPUT PROCESSING LAYER │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌────────────────────────────────────────────────────────┐ │ │ │ JSON Parser & Formatter │ │ │ │ - Extract risk_score (0-100) │ │ │ │ - Parse potential_jailbreak (bool) │ │ │ │ - Extract policy_break_points [array] │ │ │ │ - Identify attack_used (string) │ │ │ └────────────────┬─────────────────────────────────────┘ │ │ │ │ │ ┌────────────────▼─────────────────────────────────────┐ │ │ │ Visual Output Formatter │ │ │ │ - Color-coded risk display (Green/Orange/Red) │ │ │ │ - Markdown rendering │ │ │ │ - HTML formatted output │ │ │ └────────────────┬─────────────────────────────────────┘ │ │ │ │ │ ┌────────────────▼─────────────────────────────────────┐ │ │ │ Dashboard Aggregator │ │ │ │ - Risk score trends (line chart) │ │ │ │ - Result frequency (bar chart) │ │ │ │ - KPI computation (unsafe rate, top model) │ │ │ │ - Recommendations generation │ │ │ └────────────────┬─────────────────────────────────────┘ │ │ │ │ └───────────────────┼────────────────────────────────────────────────────────────┘ ▼ ┌──────────────────────────────────────────────────────────────────────────────────┐ │ 📊 OUTPUT LAYER │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │ │ │ Raw JSON │ │ Visual Analysis │ │ Analytics │ │ │ │ Output │ │ Report │ │ Dashboard │ │ │ │ │ │ (Markdown) │ │ │ │ │ │ - Structured │ │ - Risk Score │ │ - Trend Lines │ │ │ │ threat data │ │ - Jailbreak Flag │ │ - Bar Charts │ │ │ │ - Machine │ │ - Policy Breaks │ │ - KPIs │ │ │ │ readable │ │ - Attack Type │ │ - Logs │ │ │ └──────────────────┘ └──────────────────┘ └──────────────────┘ │ │ │ └──────────────────────────────────────────────────────────────────────────────────┘
╔════════════════════════════════════════════════════════════════════════════════════╗ ║ 🔄 DATA FLOW EXAMPLES ║ ╠════════════════════════════════════════════════════════════════════════════════════╣ ║ ║ ║ FLOW 1: Chat & Analysis Tab ║ ║ User Input → Router → Claude Chat → Response → Detection Engine → ║ ║ Risk Analysis → JSON Output + Visual Report → Display ║ ║ ║ ║ FLOW 2: Image Scanner Tab ║ ║ Image Upload → Extract B64 → Vision Model → Template Matching → ║ ║ SAFE/UNSAFE Classification → Display & Log ║ ║ ║ ║ FLOW 3: Text Prompt Tester Tab ║ ║ Jailbreak Prompt → Guard Model (Llama Guard / Claude) → ║ ║ Raw Response → JSON Parse → Display & Log ║ ║ ║ ║ FLOW 4: Analytics Dashboard ║ ║ Load analytics.csv → DataFrame → Risk Aggregation → ║ ║ Render Charts + KPIs → Display Dashboard ║ ║ ║ ╚════════════════════════════════════════════════════════════════════════════════════╝
┌──────────────────────────────────────────────────────────────────────────────────┐ │ 🛠️ TECHNOLOGY STACK │ ├──────────────────────────────────────────────────────────────────────────────────┤ │ │ │ Frontend: Gradio 5.49.1 (Glass Theme) │ │ Backend: Python 3.x + OpenAI Python Client │ │ API Gateway: OpenRouter.ai/api/v1 │ │ Detection: Anthropic Claude Models (Prompt-based) │ │ Data Format: JSON, CSV, Pandas DataFrame │ │ Visualization: Matplotlib (Charts), Markdown (Reports) │ │ Logging: IST Timezone Logging, CSV Storage │ │ Interface: Gradio Blocks (Multi-tab UI) │ │ Deployment: Gradio Share (share=True) + MCP Server Support │ │ │ └──────────────────────────────────────────────────────────────────────────────────┘
╔════════════════════════════════════════════════════════════════════════════════════╗ ║ 📋 COMPONENT INTERACTIONS ║ ╠════════════════════════════════════════════════════════════════════════════════════╣ ║ ║ ║ ┌──────────────┐ ┌─────────────────┐ ┌──────────────────┐ ║ ║ │ User Input │────────▶│ Gradio Frontend │───────▶│ Request Router │ ║ ║ └──────────────┘ └─────────────────┘ └────────┬─────────┘ ║ ║ │ ║ ║ ┌────────────────────────────┘ ║ ║ │ ║ ║ ┌──────────────▼──────────────┐ ║ ║ │ Detection Engine (Claude) │ ║ ║ └──────────────┬──────────────┘ ║ ║ │ ║ ║ ┌──────────────▼──────────────┐ ║ ║ │ OpenRouter Multi-Model API │ ║ ║ └──────────────┬──────────────┘ ║ ║ │ ║ ║ ┌──────────────▼──────────────┐ ║ ║ │ Analysis & Formatting Layer │ ║ ║ └──────────────┬──────────────┘ ║ ║ │ ║ ║ ┌──────────────▼──────────────┐ ║ ║ │ CSV Logging & Storage │ ║ ║ └──────────────┬──────────────┘ ║ ║ │ ║ ║ ┌──────────────▼──────────────┐ ║ ║ │ Dashboard & Output Display │ ║ ║ └─────────────────────────────┘ ║ ║ ║ ╚════════════════════════════════════════════════════════════════════════════════════╝
✅ Reminder
Falconz is intended only for ethical security testing and AI safety research as part of MCP Gradio Hackathon.
Users are responsible for complying with all laws, policies, and platform terms.
🛡️ Build safe. Test responsibly. Protect the future of AI , contact me to Xhaheen for Collab .