CyberCortex AI Agent

Overview

CyberCortex AI Agent is a fully local AI-powered cybersecurity platform designed to assist with authorized security assessments, bug bounty research, reconnaissance, security analysis, and professional report generation.

The project combines a locally hosted reasoning model with custom-built cybersecurity tools to automate repetitive tasks while supporting informed manual security testing.

Unlike traditional AI chatbots, CyberCortex AI Agent follows a structured workflow where the AI plans an assessment, coordinates multiple security tools, analyzes collected evidence, and generates a professional Markdown security report.

The project was built to explore how modern AI agents can move beyond conversation and become practical assistants for cybersecurity professionals.

Screenshots

Below are several screenshots demonstrating the CyberCortex AI Agent workflow, architecture, command-line interface, and AI-generated reporting.

CyberCortex AI Agent

Main project interface and CyberCortex AI Agent branding.

Command Line Help

CLI help output showing available commands and usage.

Running an Assessment

The agent running a scoped assessment workflow from the terminal.

AI Generated Security Report

Markdown report generated from structured security assessment evidence.

Internal Workflow

Architecture diagram showing the local AI model, workflow manager, tools, and report writer.

DeepSeek R1 Report Generation

Local DeepSeek R1 Distill 32B model reasoning over findings and preparing the report.

GitHub Repository

Public GitHub repository containing source code, documentation, and installation instructions.

Why I Built This Project

Large Language Models are excellent at reasoning, but they cannot inspect websites, crawl applications, verify security headers, or discover attack surfaces on their own.

To solve this limitation, I built CyberCortex AI Agent as a modular platform that combines AI reasoning with real-world security tooling.

The goal was to understand how AI agents can coordinate multiple tools, process structured evidence, and assist security researchers without relying on cloud-hosted AI services.

The project also provided an opportunity to gain hands-on experience with AI agent architecture, workflow orchestration, and secure software design.

Architecture

CyberCortex AI Agent is organized into several independent components.

Local AI Integration

The project runs entirely on local hardware using:

• Ollama
• DeepSeek R1 Distill 32B

Running the model locally allows sensitive assessment data to remain on the researcher's machine while providing AI-assisted reasoning without external cloud services.

Security Tools

CyberCortex AI Agent currently includes custom-built tools for:

Reconnaissance

• DNS Lookup
• HTTP Probe
• Website Crawling with Katana

Security Analysis

• HTTP Security Header Analysis
• Misconfiguration Detection
• JavaScript Secret Scanner
• Parameter Analysis
• Authorization Test Planning

Vulnerability Detection

• Nuclei Integration

Reporting

• AI-generated Markdown Security Reports

How the Workflow Operates

A typical assessment follows this process:

Target
↓
Scope Validation
↓
Planning
↓
DNS Lookup
↓
HTTP Probe
↓
Security Header Analysis
↓
Website Crawling
↓
Misconfiguration Detection
↓
JavaScript Analysis
↓
Parameter Analysis
↓
Nuclei Scan
↓
AI Report Generation

Each stage produces structured evidence that is passed to the next stage before the AI generates a final assessment.

Technologies Used

Key Features

• Fully local AI execution
• Modular architecture
• AI-assisted workflow planning
• Custom cybersecurity tooling
• AI-generated professional reports
• Command-line interface
• Documentation and installation guides
• Safety controls through Scope Guard
• Configurable environment using .env

Lessons Learned

Building CyberCortex AI Agent provided valuable experience in:

• AI agent architecture
• Local LLM integration
• Workflow orchestration
• Python application development
• Modular software design
• Security automation
• Documentation
• Open-source project management
• Git version control

One of the most important lessons was understanding that the effectiveness of an AI security agent depends on combining reasoning with reliable evidence collection. Rather than relying on assumptions, the agent gathers structured information from multiple tools before generating recommendations.

Current Status

Version 1.0

The initial release includes a fully functional AI-assisted security workflow, modular security tools, local LLM integration, AI-generated reporting, documentation, testing, and GitHub support.

Future versions will focus on improved report accuracy, historical assessment comparison, memory, plugin support, and more advanced AI agent capabilities.

GitHub

Explore the project and source code:

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