CyberStrikeAI and Automated-Vulnerability-Scanning-with-Agentic-AI
Both tools are AI-powered security testing platforms, suggesting they are competitors offering different approaches to automated vulnerability scanning and orchestration.
About CyberStrikeAI
Ed1s0nZ/CyberStrikeAI
CyberStrikeAI is an AI-native security testing platform built in Go. It integrates 100+ security tools, an intelligent orchestration engine, role-based testing with predefined security roles, a skills system with specialized testing skills, and comprehensive lifecycle management capabilities.
Based on the README, here's a technical summary that goes deeper: --- Uses native MCP (Model Context Protocol) with HTTP/stdio/SSE transports and external federation to connect AI agents directly to security tools, enabling conversational control flow through an orchestration engine that supports multi-agent delegation patterns (Eino DeepAgent). Includes vector-search knowledge base, attack-chain graph replay with risk scoring, WebShell management for post-exploitation, and optional Burp Suite integration via plugin architecture; persists all audit trails and task queues in SQLite with password-protected web UI. --- **Word count: ~65 | Key technical details**: MCP protocol variants, multi-agent orchestration, vector search, attack graphs, WebShell C2 capabilities, plugin extensibility, SQLite backend
About Automated-Vulnerability-Scanning-with-Agentic-AI
salah9003/Automated-Vulnerability-Scanning-with-Agentic-AI
The system consists of multiple AI agents that collaborate to strategize, generate commands, and execute scans based on the client's description, without the need for human intervention.
Multi-agent architecture with specialized roles (strategy generation, command execution, error handling, reporting) coordinated via GPT-4 Turbo to autonomously scan targets on Kali Linux using standard penetration testing tools. The system iteratively refines scanning strategies based on command output feedback until completion, with all interactions and findings logged to markdown reports. Designed as a proof-of-concept demonstrating LLM-based agent collaboration for cybersecurity workflows, though interactive tools like msfconsole have execution limitations.
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