Kimi: Attack Surface Discovery Engine

Developed with 💜 by @alechilczenko

Project Context: This project serves as a portfolio piece demonstrating concepts in offensive security, concurrency, and distributed architecture.

1. Introducing Kimi: The Origin Story

What was Kimi ?

During my early days venturing into the world of offensive security, I was always fascinated by how major search engines like Shodan indexed information from millions of devices. This concept kept spinning in my head; I needed to understand the engineering behind it by simulating it myself.

Kimi is the result of that curiosity. It is a continuous reconnaissance scanner designed to prioritize the analysis of external attack surface exposure. It performs large-scale, port-oriented scanning to collect information from services connected to the Internet.

My goal was to create an efficient architecture: the implementation of threads and queues was designed to maximize performance per scan cycle, significantly reducing execution time for massive IP ranges.

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2. Key Aspects

Kimi serves as a proof of concept in critical areas:

• Concurrency: Usage of Threads and Queues in Python to orchestrate up to ≈500 simultaneous network I/O operations.

• Decoupled Architecture: Design of a distributed system (Engine → API → DB), separating the execution of the scan from data persistence.

• Networking: Low-level socket programming for TCP/UDP handshakes and precise timeout management.

• NoSQL Databases: Implementation of MongoDB to store scan results, which are inherently variable and unstructured.

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3. The Learning Curve

Revisiting Kimi today allows me to apply a modern engineering and ethical perspective to my initial work:

• Technical Evolution: The biggest challenge was mastering threading in Python. Today, I recognize that the most scalable solution would be migrating the Engine to asynchronous programming (asyncio) to avoid GIL overhead and scale concurrency even further.

• Ethical Maturity: The most important lesson has been professional responsibility. The project evolved from a curiosity-driven experiment to a demonstration tool. Kimi is strictly limited to ethical testing on controlled networks, highlighting my commitment to legal and responsible security practices.

4. Architecture & Workflow

The system is divided into the Engine (Scanner) and the API (Persistence).

Data Architecture

MongoDB acts as the primary ingestion store. The architecture is designed to be compatible with Elasticsearch / Kibana (ELK) integration, enabling full-text search capabilities and analytical visualizations of the collected data.

Concurrency Examples (CLI)

5. Contact & Legal

• Deployment: The project is fully containerized with Docker.

• License: Apache 2.0.

• Contact: [email protected]

• Legal Disclaimer: Kimi is presented solely for educational purposes and technical demonstration. Usage outside of controlled environments is the sole responsibility of the user.