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metadata
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 Safeguard your Agentic Apps!!
tags:
  - building-mcp-track-enterprise
  - mcp-in-action-track-enterprise
  - building-mcp-track-consumer
  - mcp-in-action-track-consumer
  - 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=HTEs5Sw-ID0

🌐 Social media - LinkedIn and twitter Post:
Public announcement .
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

https://x.com/SalluMandya/status/1993948272780825003?s=20

🌐 Google CO:lab:
https://colab.research.google.com/drive/1PSuPQ35UZntKcUBd43QtjrsRLVvHJYlm?usp=sharing

🌐 HF Blog:
https://huggingface.co/blog/Xhaheen/falconz-mcp-hackathon

🏷️ 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

  1. User enters a message in the Chat tab
  2. Falconz sends the message to the selected LLM model
  3. The model responds normally
  4. The response is passed through the risk analysis engine
  5. A JSON risk score + visual report is generated
  6. Conversation & analysis logs are stored for analytics

✅ 2️⃣ Text Prompt Tester Flow

  1. User inputs a jailbreak/prompt-injection test prompt
  2. Falconz sends it directly to the selected guard model
  3. The raw model response is returned (no chat history)
  4. Users compare responses to evaluate model safety behavior

✅ 3️⃣ Image Scanner Flow

  1. User uploads an image containing text or hidden instructions
  2. Falconz extracts image content and sends it to a vision model
  3. The model evaluates the content for injection threats
  4. Output is classified as SAFE or UNSAFE

🧑‍💻 Authors

📝 License

This project is licensed under the MIT License.

📝 Architecture

View Architecture Diagram

🏗️ System Architecture Overview

Falconz is a multi-layered LLM security platform with the following core components:

📊 Component Breakdown

1️⃣ Frontend Layer (Gradio UI)

  • Chat & Analysis Tab - Real-time chat with integrated threat detection
  • Image Scanner Tab - Vision-based prompt injection detection
  • Text Prompt Tester Tab - Custom jailbreak testing interface
  • Analytics Dashboard Tab - Real-time threat trends and metrics
  • Learning Hub Tab - Educational resources for red teaming

2️⃣ Detection Engine Layer (Claude-Powered)

  • falcon_prompt_text - Text-based jailbreak and prompt injection detection
  • Falcon_prompt_image - Vision-based injection scanning (SAFE/UNSAFE classification)
  • prompt_injection_templates - Top 10 OWASP-inspired jailbreak patterns
  • Risk Scoring Engine - Generates risk scores (0-100) with policy violation flags

3️⃣ Multi-Model API Layer (OpenRouter Gateway)

Detection Models:

  • Claude Sonnet 4.5
  • Claude Opus 4.1
  • Claude Haiku 4.5
  • Llama Guard 4

Chat Models:

  • Google Gemini 2.5
  • OpenAI GPT-4o
  • Mistral Medium
  • Microsoft Phi-4

Vision Models:

  • Claude Sonnet 4.5
  • Google Gemini 2.5
  • OpenAI GPT-4o
  • Phi-4 Multimodal

4️⃣ Data Storage Layer (CSV & Logging)

  • analytics.csv - Logs timestamps, detection results, and models used
  • Prompts.csv - Customizable prompt injection templates
  • Prompts_updated.csv - Modified templates with dynamic replacements

5️⃣ Analysis Engine Layer (Processing & Formatting)

  • JSON Parser - Extracts risk_score, jailbreak flags, policy breaks, attack types
  • Visual Formatter - Color-coded risk display (Green/Orange/Red), Markdown rendering
  • Dashboard Aggregator - Computes trends, KPIs, and generates recommendations

6️⃣ Output Layer (Results & Reports)

  • Raw JSON Output - Structured threat detection data
  • Visual Analysis Report - Color-coded risk scores with policy violations
  • Analytics Dashboard - Interactive charts, trends, and security insights

🔄 Data Flow 

User Input (Frontend) 
    ↓
Request Router (Message/Image/Prompt Handler)
    ↓
Detection Engine (Claude Analysis)
    ↓
Multi-Model API (OpenRouter Gateway)
    ↓
External APIs (Google, OpenAI, Meta)
    ↓
Data Storage (CSV Logging)
    ↓
Analysis Engine (JSON/Format Processing)
    ↓
Output Layer (JSON, Visual Report, Dashboard)

🛠️ Technology Stack

Component Technology
Frontend Gradio 5.49.1 (Glass Theme)
Backend Python 3.x + OpenAI Client
API Gateway OpenRouter.ai/api/v1
Detection Anthropic Claude Models
Data Format JSON, CSV, Pandas
Visualization Matplotlib, Markdown
Logging IST Timezone + CSV Storage
Deployment Gradio Share + MCP Support

✨ Key Features

✅ Real-time Detection - Jailbreak and prompt injection scanning
✅ Multi-Model Testing - Compare safety across 15+ LLM providers
✅ Vision Scanning - Image-based threat detection
✅ Customizable Templates - Top 10 OWASP-inspired attack patterns
✅ Risk Scoring - Automated 0-100 risk assessment
✅ Analytics Dashboard - Trend visualization and KPI tracking
✅ MCP Integration - Enterprise-grade agentic workflow support
✅ Ethical Red Teaming - Secure, responsible AI safety testing

🎯 Use Cases

  • AI Safety Teams - Test LLM robustness against adversarial prompts
  • Security Researchers - Benchmark jailbreak techniques across models
  • DevOps Engineers - Monitor LLM-based applications for injection risks
  • Enterprise Security - Validate agentic systems before production deployment

📄 License

MIT License - See project repository for details

✅ 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 .