Nebula is a local-first security assessment workbench designed for penetration testing and red teaming operations. It functions as a desktop application built with a Tauri shell (Rust) wrapping a React/TypeScript frontend and a Python FastAPI backend sidecar. The system manages security engagements, scope policies, evidence collection, and AI-assisted analysis, emphasizing operator control, auditability, and data privacy through local-first execution and content-addressed artifact storage. The architecture follows a sidecar pattern where the Python Core service handles database operations (SQLite with Alembic), containerized command execution (Kali Linux via Docker/Podman), and AI provider integration. The frontend manages the UI state and terminal sessions via WebSocket. Key data flows involve user actions triggering API calls that validate against scope policies before executing commands in isolated containers, with all outputs stored as immutable artifacts for evidence integrity. The codebase demonstrates strong engineering practices with extensive test coverage (Pytest, Vitest, Playwright), a robust diagnostic system with secret redaction, and a policy engine for deterministic scope enforcement. It supports multiple AI providers and includes features for knowledge indexing, mission orchestration, and report generation. The project is actively maintained with recent commits and zero security alerts, though it is currently in alpha release.
Pentest-Swarm-AI is an open-source autonomous penetration testing tool built on a stigmergic swarm architecture. It coordinates multiple LLM-driven specialist agents (recon, classifier, exploit, report) via a shared blackboard with pheromone decay, enabling emergent attack paths without a central planner. The system integrates 30+ external security tools (nmap, nuclei, sqlmap, etc.), multiple LLM providers (Claude, OpenAI, Ollama, Gemini, LM Studio), and bug bounty platforms (HackerOne, Bugcrowd, Intigriti) for automated scope import and report submission. The architecture is a multi-agent system with a scheduler that dispatches agents based on blackboard predicates and pheromone levels. Key entry points include a CLI (Cobra commands: scan, init, playbook, submit, etc.), an API server (Fiber), a WebSocket hub for live streaming, a Next.js web dashboard, and an MCP server for IDE integration. Data flows: agents write findings to the blackboard with pheromone weights; the scheduler reads the blackboard and triggers agents whose predicates match; agents read findings, process them, and write new findings, creating a stigmergic feedback loop. External dependencies include PostgreSQL with pgvector for durable blackboard storage, LLM API endpoints, and the installed security tool binaries. The codebase demonstrates high craftsmanship with clean abstractions (tool adapter pattern, LLM provider interface, blackboard interface), extensive testing (unit, integration, e2e, benchmarks, LLM eval), and safety features (scope enforcement, provenance signing, rate limiting, false-positive caching). It is actively maintained with recent commits, CI/CD pipelines, and community engagement.