Open Innovation Mentora BuildersSubmitted July 18, 2026

Talos, an AI powered vulnerability detection and auto-remediation via MCP

An MCP app on the Model Context Protocol built by Mentora Builders at the Amrita University Amritapuri campus NitroStack × MCP To The Moon hackathon and deployed on NitroStack.

About this project

Talos is an AI-powered cybersecurity assistant and brute-force detection platform built for defensive security analysis, incident response, and practical security education. It combines machine-learning based authentication log analysis with a broad security toolbox for passive website scanning, CVE lookup, IP investigation, password auditing, JWT decoding, phishing/link-safety checks, academic research discovery, local resource search, report generation, alerting, and mitigation planning. What makes Talos special is its NitroStack-powered MCP server, which exposes these security capabilities as structured Model Context Protocol tools. Instead of giving generic text-only answers, AI assistants can call the correct Talos tool directly, stream live tool activity, generate evidence-backed reports, and move from detection to investigation and remediation in one workflow. The system uses a Python security backend with a TypeScript/NitroStack MCP layer, making it suitable for local development, cloud hosting, and AI-integrated security operations. Talos has strong market potential as organizations increasingly need AI-native security tools that make threat detection, investigation, reporting, and remediation faster, more accessible, and easier to operationalize.

Open Innovation track

Solve any real-world problem with AI, regardless of industry or domain.

Team Mentora Builders

  • ARSHA B MOHANLead

  • Rithvik prasannakumar

  • Sajeev K

  • Asvin Pradeep

Frequently asked questions

What does Talos, an AI powered vulnerability detection and auto-remediation via MCP do?
Talos is an AI-powered cybersecurity assistant and brute-force detection platform built for defensive security analysis, incident response, and practical security education. It combines machine-learning based authentication log analysis with a broad security toolbox for passive website scanning, CVE lookup, IP investigation, password auditing, JWT decoding, phishing/link-safety checks, academic research discovery, local resource search, report generation, alerting, and mitigation planning. What makes Talos special is its NitroStack-powered MCP server, which exposes these security capabilities as structured Model Context Protocol tools. Instead of giving generic text-only answers, AI assistants can call the correct Talos tool directly, stream live tool activity, generate evidence-backed reports, and move from detection to investigation and remediation in one workflow. The system uses a Python security backend with a TypeScript/NitroStack MCP layer, making it suitable for local development, cloud hosting, and AI-integrated security operations. Talos has strong market potential as organizations increasingly need AI-native security tools that make threat detection, investigation, reporting, and remediation faster, more accessible, and easier to operationalize.
Who built Talos, an AI powered vulnerability detection and auto-remediation via MCP?
Talos, an AI powered vulnerability detection and auto-remediation via MCP was built by team Mentora Builders at the Amrita University Amritapuri campus NitroStack × MCP To The Moon hackathon, in the Open Innovation track.
What is an MCP app and how is it built?
An MCP app is an application built on the Model Context Protocol — an open standard that lets AI agents connect to tools, data, and APIs. This project exposes MCP tools and resources that agentic AI systems can call. It was built and deployed on NitroStack, the full-stack platform for shipping MCP apps and servers.