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.
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.
CDK (Container Docker/Kubernetes Exploitation Toolkit) is a Go-based CLI penetration testing tool designed for container and Kubernetes environments. It evaluates security posture by running extensive checks against container runtime configurations, K8s API permissions, cloud metadata, and kernel exploit possibilities. It then executes a plethora of exploits covering container escape (cgroups, mount, eBPF, runc/containerd CVEs), K8s credential access (secrets, configmaps, tokens), persistence (backdoor daemonsets, cronjobs, shadow API server), privilege escalation (service account token theft, CVE-2026-31431), and remote control (reverse shells, kubelet exec). The binary bundles stripped-down versions of common tools (kubectl, netcat, vi, etc.) for post-exploitation use. The architecture follows a plugin pattern where exploits register themselves via init(), and evaluation uses a category-based check system. The codebase shows signs of organic growth with some legacy cruft (deprecated auto-escape task, commented-out code, future-dated CVEs) and moderate technical debt (global mutable state, panics, outdated Go idioms), but remains functional and actively maintained for penetration testers and security researchers.