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metadata
title: TDAgent
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.31.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - agent-demo-track
short_description: AI-driven TDAgent to automate threat analysis with MCP tools

Hackathon Participation: Cybersecurity AI Agents

This project is our contribution to Tracks 1 and 3 of the Agents-MCP-Hackathon, focused on applying AI technologies in the cybersecurity domain. Our aim is to develop solutions that improve the operational efficiency in cybersecurity through automation and data-driven insights.

Team Overview

Our team is part of the AI division in our company's cybersecurity department. We focus on implementing AI-based solutions to assist cybersecurity operations. Our team members include:

  • Pedro Completo Bento
  • Josep Pon Farreny
  • Sofia Jeronimo dos Santos
  • Rodrigo Dominguez Sanz
  • Miguel Rodin

Project Goals

We are exploring the application of AI agents to aid cybersecurity analysts in threat data enrichment and threat analysis. Our main goals are:

  1. To experiment with agentic technologies like Gradio and MCP.
  2. To explore how AI can improve data enrichment capabilities in threat analysis.
  3. To develop autonomous agents capable of API interaction, data enrichment, and threat evaluation.

Track 1: MCP Tool / Server

In Track 1, we developed TDAgentTools, a Gradio-powered MCP server offering a set of public cybersecurity intelligence tools. This tool is designed to assist cybersecurity professionals in their threat analysis and response tasks.

Access TDAgentTools here: TDAgentTools Space

Track 3: Agentic Demo Showcase

For Track 3, we created TDAgent, an AI agent with a chat interface that connects to MCPs, defaulting to TDAgent MCP. The agent utilizes TDAgentTools or other MCP servers to gather additional threat intelligence, providing enriched data for more comprehensive threat evaluations.

Usage and Purpose

  • TDAgentTools: Provides cybersecurity professionals with essential analysis tools via a user-friendly interface.
  • TDAgent: Facilitates interactive AI-supported threat analysis, enhancing efficiency, by leveraging data from MCP servers for improved insights.

Our work aims to reduce the manual effort involved in threat analysis, allowing cybersecurity teams to focus on strategic activities by utilizing AI for operational tasks.

Conclusion

This project seeks to demonstrate the practical applications of AI agents in cybersecurity, providing tools and frameworks to improve security operations.

TDA Agent

Development setup

To start developing you need the following tools:

To start, sync all the dependencies with uv sync --all-groups. Then, install the pre-commit hooks (uv run pre-commit install) to ensure that future commits comply with the bare minimum to keep code readable.

Old content

An example chatbot using Gradio, huggingface_hub, and the Hugging Face Inference API.