MindFort AI (YC X25) | Backend Software Engineer, AI Researcher | Los Angeles, CA | Full-Time I'm a founding engineer at

The gap in what exists

While traditional pen-testing and DAST/SAST tools exist, the 'autonomous AI agent' approach to finding, exploiting, and patching vulnerabilities is a novel and potentially more efficient paradigm, especially for rapid iteration in development cycles.

Wedge to win

Sell to large enterprises with complex software portfolios and high-velocity development, offering a continuous, autonomous security layer that reduces manual pen-testing costs and speeds up vulnerability remediation.

Reputation value (worth doing free for proof) — 75/100

Contributing to or building an early-stage open-source tool that demonstrates AI's capability in cybersecurity would build significant credibility within the AI/security community, useful for future consulting or product ventures.

Likely monetization

B2B SaaS, usage-based

Incumbents to beat

SynkCheckmarx

You are a senior full-stack software engineer and AI architect. Your task is to develop a minimal viable product (MVP) for an autonomous AI security agent that can identify and exploit a specific, well-known vulnerability type (e.g., SQL Injection) in a simple web application. The goal is to demonstrate the core capability of an AI agent to 'pen test' a target. 

Use Next.js 16 App Router (React 19, Tailwind v4) for a frontend control panel, a Node.js backend with Express for the agent orchestration, and Python for the core AI agent logic leveraging LangChain/LangGraph. A Neon Postgres database will store agent logs and discovered vulnerabilities. The target application should be a simple, intentionally vulnerable Node.js/Express web app that you will also create. 

**MVP Steps:**
1.  **Vulnerable Target Application:** Create a simple Node.js/Express application with a single endpoint vulnerable to SQL Injection. This app should expose a basic API that accepts user input, which is then directly concatenated into a SQL query without proper sanitization.
2.  **Agent Orchestration Backend (Node.js):** Develop a Node.js/Express API that receives a target URL and initiates the AI agent's scan. This backend will manage the communication between the frontend and the Python AI agent.
3.  **AI Agent (Python/LangChain):** Implement a Python agent using LangChain or LangGraph. This agent should:
    *   Take the target URL from the Node.js backend.
    *   Formulate SQL Injection payloads.
    *   Send these payloads to the vulnerable endpoint.
    *   Analyze the responses for indicators of successful exploitation (e.g., error messages, unexpected data leakage).
    *   Report findings (vulnerability identified, payload used, proof of concept) back to the Node.js backend.
4.  **Frontend Control Panel (Next.js):** Build a simple Next.js interface where a user can input the target application's URL and trigger a scan. Display the scan results (vulnerabilities found, details) in a clear format.
5.  **Data Storage (Neon Postgres):** Store scan configurations and results in Neon Postgres. The table should include fields for target_url, vulnerability_type, payload_used, exploitation_proof, and timestamp.

**Build/Verify Gate:** The system successfully identifies a SQL Injection vulnerability in the provided target application and displays the exploited payload and proof in the frontend. Ensure all components communicate correctly and error handling is robust.