CyberStrikeAI: an AI-native security testing platform with integrated C2 and multi-agent orchestration for automated vulnerability discovery

What it solves

CyberStrikeAI is an AI-native security testing platform designed to automate the end-to-end process of vulnerability discovery and attack-chain analysis. It bridges the gap between conversational AI and technical security execution by integrating a vast array of security tools with an intelligent orchestration engine, allowing security teams to move from natural language commands to actionable results in an auditable and collaborative environment.

How it works

Built in Go, the platform uses an AI decision engine compatible with models like GPT, Claude, and DeepSeek to orchestrate security tasks. It leverages the Model Context Protocol (MCP) for tool integration and the CloudWeGo Eino framework for agent orchestration, supporting both single-agent and multi-agent (Deep, Plan-Execute, Supervisor) configurations. The system integrates over 100 security tools across the kill chain, manages vulnerabilities via a dedicated tracking system, and includes a built-in lightweight Command & Control (C2) framework and WebShell management for authorized engagements.

Who it’s for

It is designed for security professionals, penetration testers, and CTF players who need an automated, AI-driven environment to conduct authorized security assessments and vulnerability research.

Highlights

• AI-Driven Orchestration: Supports single and multi-agent workflows (coordinator, planner, supervisor) to automate complex attack chains.
• Extensive Toolset: Integrates 100+ prebuilt security tools covering network scanning, web apps, cloud security, binary analysis, and post-exploitation.
• Native MCP Support: Implements MCP with HTTP/stdio/SSE transports for seamless tool and external service federation.
• Built-in C2 & WebShell: Includes a lightweight Command & Control framework and WebShell manager for managing authorized sessions and implants.
• Role-Based Testing: Predefined roles (e.g., Penetration Testing, CTF) with custom prompts and tool restrictions to focus AI behavior.
• Human-in-the-Loop (HITL): Approval workflows for sensitive tool executions to ensure safety and control.
• RAG Knowledge Base: Embedding-based vector retrieval for security knowledge management.