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.
CyberStrikeAI is a full-stack, AI-native cybersecurity operations platform designed for autonomous penetration testing and red teaming. It orchestrates multiple AI agents using the CloudWeGo Eino ADK framework to plan and execute complex security assessments. The platform manages its own C2 (Command & Control) infrastructure with encrypted beacons and listeners, integrates with over 100 security tools via the Model Context Protocol (MCP), and maintains a persistent knowledge base and project fact graph. It features a comprehensive governance layer including Role-Based Access Control (RBAC), Human-in-the-Loop (HITL) approval workflows, tool execution guards, and detailed audit logging. The architecture is a monolithic Go-like backend serving a web SPA, with plugins for Burp Suite and Chrome. It supports multi-agent orchestration modes (single, deep, plan-execute, supervisor), a graph-based workflow engine, and integrations with various chat platforms (WeChat, Discord, Telegram). The codebase demonstrates high craftsmanship in its execution governance, process isolation, atomic update mechanism, and database schema reconciliation. A significant technical concern is the use of a non-standard Go dialect (featuring `func`, `type`, and `map` literals) tied to the Eino framework. This creates a hard dependency on a specific toolchain and represents a major drift risk from the standard Go ecosystem, limiting portability and the ability to use standard tooling. Despite this, the project is actively maintained with extensive test coverage and a clear focus on production-grade security automation.
This repository is a manually curated awesome-list/registry of open-source security scanners and offensive/defensive tools. It spans traditional security testing categories (web, mobile, binary, smart-contract, IoT, APT) and an actively expanded section for AI/LLM-agent security. The actual content is a set of near-identical English, Chinese, and Spanish README files containing hundreds of one-line tool entries with shields.io badges for language, last commit, stars, and license. There is no application runtime, no build pipeline, and no test suite; the product is a navigable knowledge base rather than software. The repo is therefore classified as data_research only in the sense that it is a structured, human-maintained metadata registry, not a data pipeline. The primary data lineage is manual: maintainers sample external GitHub projects, annotate each with a one-sentence description and category, and publish the result as markdown. The taxonomy serves as an analytical schema, answering questions such as which open-source scanners exist for a given domain, which are active, and what maturity signals (stars, license, last commit) they carry. It does not provide quantitative benchmarks or automated validation, and the data is not exposed in machine-readable form beyond the markdown itself. Reuse is straightforward for human readers, but automation requires parsing the markdown and accepting that some badges or links may be stale or inconsistent. The repository is actively curated, with recent emphasis on AI security, a documented commit convention, and multilingual READMEs. Its main risks are extrinsic: link rot, renamed or abandoned upstream tools, and the CC BY-NC-ND license, which restricts derivative redistribution. Technical drift is moderate because some older categories and tools are outdated even though the overall list is kept current.
This repository is a curated directory of cybersecurity tools and resources, organized into categories such as AI security, API pentesting, C2 frameworks, forensics, OSINT, and more. It is built as a static documentation site using Retype, with markdown pages listing external links and brief descriptions. The repository contains no executable application code, data pipelines, or analytical models. Its primary function is to serve as a reference index for security professionals, maintained through periodic updates to links and categories. The codebase is essentially a collection of markdown files and configuration for the static site generator, with a large set of default password text files included as a resource. There is no software architecture, entry points, or data flows beyond the static site generation. The repo is well-organized and regularly updated, but it offers no reusable software components or novel engineering patterns.