Strix is an open-source, AI-powered penetration testing tool that orchestrates multiple LLM-driven agents to autonomously discover and validate security vulnerabilities in web applications, APIs, source code, and infrastructure. The system operates as a CLI application with three interface surfaces: a headless/non-interactive mode for CI/CD pipelines, a feature-rich Go Bubble Tea TUI for interactive scanning, and a local React web viewer for reviewing results. Scans run inside Docker sandboxes with a Caido interception proxy sidecar, allowing agents to execute shell commands, browse applications, modify requests, and apply patches. The architecture follows a multi-agent coordination pattern where a root agent delegates tasks to child agents, tracks coverage, manages budgets, and produces comprehensive reports including CVSS-scored vulnerabilities, proof-of-concept exploits, and fix recommendations. The codebase demonstrates strong engineering practices with extensive test coverage, atomic file operations, output bounding, MCP tool integration, and a sophisticated IPC protocol between the Python backend and Go TUI. Key features include: multi-agent orchestration with parent/child messaging and budget enforcement; Docker sandboxed execution with Caido proxy integration; support for multiple LLM providers (OpenAI, Anthropic, LiteLLM, Bedrock, etc.); MCP (Model Context Protocol) tool integration; SARIF and PDF report generation; cloud platform integration for managed scans; and a local web viewer with live transcript and vulnerability triage. The repository is primarily an application but includes data-oriented components like LLM cost tracking, coverage ledgers, and vulnerability deduplication using LLM-based comparison.
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.
This repository is a comprehensive, curated collection of cybersecurity references, scripts, tools, code, labs, and training resources, primarily authored by Omar Santos. It serves as supplemental material for his books, video courses, and live training, covering a vast range of domains including offensive security, defensive security, application security, cloud/container security, hardware/embedded security, AI/ML security, and certification preparation (Cisco, CompTIA, ISC2, etc.). The repo is hybrid in nature: while it contains hundreds of runnable scripts and tools (e.g., network scanners, exploit examples, API security testers, AI-powered log analyzers), its dominant character is that of an educational knowledge base and reference library rather than a deployable application. The content is organized into clear domain directories, with extensive READMEs acting as curated indexes of tools, papers, talks, and labs. The AI security section is particularly well-developed, including course materials on agentic AI, prompt engineering, and the Foundry Security Spec. The repository is actively maintained, with recent commits and automated CI workflows, though the breadth of content introduces risks of link rot and technology churn in fast-moving areas like AI frameworks and certification blueprints.
This repository provides a collection of 52 specialized AI agent definitions for penetration testing and red team operations, designed to be used with Claude Code. It is distributed as a Claude Code plugin, with an interactive installer (install.sh), a SQLite findings database (db/findings.sh) for persisting engagement data, and a versioned migration system. The agents are defined as Markdown files with YAML frontmatter, each containing structured system prompts that enforce scope validation, OPSEC tagging, evidence handling, and MITRE ATT&CK mapping. The system includes a swarm orchestrator for multi-agent coordination, a scope guard for safety, and a two-tier safety model (advisory vs. Bash-capable agents). While primarily a configuration/instruction set rather than a traditional software application, it demonstrates strong engineering practices in prompt architecture, database schema design, and distribution automation. The repository is actively maintained with CI validation, shellcheck, and unit tests for the database scripts.
This repository is a large collection of standalone Python scripts designed for the Termux environment on Android. It is marketed as an educational cybersecurity toolkit but contains functional phishing pages, credential harvesters, location trackers, camera/mic capture scripts, and a trojan for data exfiltration. The codebase also includes some legitimate utilities such as a password manager, file converter, network scanner, and backup tool. The architecture is highly fragmented: each script is self-contained with its own dependency auto-installation, Flask server, and cloudflared tunnel for public exposure. There is no shared module structure, and the code is heavily duplicated across English and Greek translations. The overall quality is low due to copy-paste patterns, hardcoded secrets, suppressed error logging, lack of testing, and reliance on external services like cloudflared. The repository poses significant legal and ethical risks due to its phishing and surveillance capabilities.