CyberStrikeAI: AI-Native Platform for Fully Automated Red Team Penetration Testing

Introduction

CyberStrikeAI is an AI-native security testing platform built with Go, designed to automate the full penetration testing lifecycle. It targets cybersecurity practitioners, security R&D engineers, and security students seeking to leverage AI for tooling and automation upgrades.

Pain Points of Traditional Penetration Testing

Tools are siloed (e.g., Nmap scanning → manual analysis → Metasploit exploitation) with no linkage.

Attack path planning relies on individual experience, making it difficult to systematize for complex targets.

Report writing is time-consuming, and results are hard to trace and reuse.

CyberStrikeAI Innovations

AI-native : Built from the ground up around LLM + MCP protocol, not merely an AI wrapper on traditional tools.

Go-based : Compiles to a single binary with zero dependencies and high performance.

100+ integrated tools : Covers the complete attack chain from information gathering to post-exploitation.

Full-process automation : Conversational commands → tool orchestration → vulnerability discovery → attack chain analysis → knowledge retrieval → result visualization.

Core Architecture: Single-Agent ReAct and Multi-Agent Orchestration

Single-Agent Mode (ReAct)

The AI agent follows an Observe → Reason → Act → Reflect loop to conduct penetration testing.

API endpoints: /api/agent-loop and /api/agent-loop/stream (SSE streaming output).

Multi-Agent Mode (Based on CloudWeGo Eino)

Three orchestration modes selectable via the orchestration field in the request body.

API endpoint: /api/multi-agent/stream (requires enabling multi_agent.enabled: true in config.yaml).

Conclusion

CyberStrikeAI provides a comprehensive, auditable, and collaborative environment for automated red team operations, combining a rich toolset with advanced AI agent orchestration.