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.
This repository provides an all-in-one CLI/TUI application for launching and managing security testing tools. It features a curated catalog of 215+ tools across 21 categories, defined declaratively in YAML files and loaded into Python tool classes. The interactive REPL supports commands like /run, /search, /tags, /ai, /goal, and /find, with a rich console interface using prompt_toolkit and Rich. A headless engagement mode runs YAML-defined pipelines (e.g., subfinder → httpx → nuclei) and generates structured findings and Markdown reports. AI features are opt-in and use OpenAI-compatible endpoints or local Ollama, with built-in safety guardrails (charter injection, sanitization, anti-fabrication checks) and graceful degradation to offline behavior. The architecture is monolithic but modular, with clear separation between the tool catalog, execution engine, AI integration, and discovery module. The codebase demonstrates strong engineering practices: safe subprocess execution (list-form, cwd, skip if present), declarative YAML overlays, a fixed tag taxonomy enforced by tests, and extensive test coverage including monkeypatching of subprocess and AI transport. While the catalog inherently drifts as upstream tools become archived, the project includes audit scripts to manage this. Overall, it is a well-crafted, production-quality application for authorized security testing.
CodExt is a Python library and command-line tool that extends the standard library's codecs with over 120 custom encodings for data transformation. It provides encoding/decoding functionalities across domains including base encodings (baseN, base45, base85, etc.), cryptography (Vigenère, RSA, XOR), compression (gzip, LZ77/LZ78), steganography, and language-based transformations (Leetspeak, Morse, Braille). The system features a heuristic-based guess mode for automated multi-layered decoding and a macro system for chaining codecs. Architected as a plugin-based library, it allows dynamic registration of new codecs and is designed for CTF, security, and data obfuscation tasks. The codebase demonstrates strong modularity with clear separation of concerns between core functionality, codec implementations, and utility modules.
HAITI (HAsh IdenTIfIer) is a Ruby library and CLI tool for identifying hash types from input strings. It matches against a comprehensive database of over 675 hash types (including modern algorithms like SHA3, Blake2, and VeraCrypt) using regex patterns. The library provides concise output with hashcat mode numbers, John the Ripper format names, and example hashes. Multiple CLI entry points exist: the main `haiti` command, `haiti-parsable` for script-friendly output, `haiti-fzf` for interactive selection, and `hashcat-haiti`/`john-haiti` for converting hashes into hashcat/JtR formats. The codebase is well-structured, with clean separation between the identification engine, data definitions, and CLI wrappers. It includes comprehensive documentation (man pages, YARD API docs, a docsify site) and is packaged for numerous Linux distributions and Homebrew. The identification logic is straightforward but effective: iterate over a JSON array of prototypes, apply each regex to the input, and return matching types. The data file (`prototypes.json`) is manually maintained and extended with each release.
ctf-tools is a Nix flake that packages and manages CTF/security research tools. It provides a CLI interface (manage-tools) for installing, upgrading, and removing tools from a user's Nix profile. The system isolates each tool in its own derivation, builds from source or fetches prebuilt binaries from a Cachix cache, and handles complex packaging requirements like tools needing writable directories at runtime (BeEF, libc-database) or proprietary software requiring user-provided binaries (IDA). The architecture is modular with separate derivations for each tool, CI/CD for automated building and caching, and thoughtful handling of runtime dependencies through wrapper scripts.
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.