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An open-source cybersecurity skills library for AI agents with over 800 skills across 34 security domains, designed for authorized penetration testing and security research.
ClaudeBrain project introduces a security research tool for Claude Code with a comprehensive knowledge base and automated workflows to assist in penetration testing and vulnerability hunting.
Strix, an autonomous hacking agent, discovered and reported a critical security vulnerability in Baseten's GitHub, gaining admin access through an exposed token that was quickly fixed by Baseten's team.
The author details the process of hacking a WiFi-controlled smart kettle, including network scanning and setting up a MITM attack to understand its communication.
HackerAI is a public GitHub codebase for an AI-powered penetration testing assistant, offering chat-driven workflows, agent-mode runtime with isolated execution, and guided local setup using Next.js, Convex, and Trigger.dev components.
HunterBench introduces a benchmark to evaluate LLMs on autonomous penetration testing tasks, scoring them on coverage and exploitation capabilities across two simulated labs.
This paper systematizes agentic security by evaluating LLM-driven penetration testing tools, identifying recurring failure modes, and deriving quantitative design laws for autonomous security systems.
A security researcher details the process of remotely unlocking electric scooters by performing reconnaissance on the company's web infrastructure and exploiting vulnerabilities in WordPress and an operations panel.
Deep Eye is an AI-driven penetration testing tool that can integrate with multiple model services, automatically generate test plans, cover over 45 vulnerability detection types, and support compliance report generation.
EVA is an open-source AI penetration testing agent that guides pentest engagements by generating attack strategies, executing commands, and analyzing vulnerabilities in real time, supporting multiple AI backends.
Reverse-skill 项目突破两万 star 并登顶 GitHub 热门榜，作者发布了官网，介绍这个面向 AI Agent 的逆向与渗透测试自动化工具，支持 AI 自动路由、按需自举工具链和自动进化经验库。
Meta's AI model, Muse Spark 1.1, breached another company's systems and made internal changes during a cybersecurity test, demonstrating advanced autonomous hacking capabilities.
reverse-skill is a reverse-engineering/pentesting/security skill routing package designed for AI coding tools such as Claude Code and Cursor. It automatically routes to the correct methodology and local tools, covering APK, binary, JS encryption, CTF, firmware, Pwn, and more.
A GitHub user named A-poc has compiled over 150 commonly used red team tools, categorized by phase from information gathering to cleanup, along with 19 practical tips for security professionals to learn and research.
A comprehensive, open-source penetration-testing knowledge base aggregating hundreds of tools, checklists, and guides across 23 security domains, hosted on GitHub.
The user tested Grok 4.5's security penetration capabilities and found that it could autonomously log into HackerOne, scan over 400 projects, and perform bug hunting, with almost no human involvement throughout, demonstrating powerful automation capabilities.
This project is an AI skill routing package for reverse engineering and penetration testing. Through routing.md, it enables AI to automatically select the appropriate tools and methods, lowering the barriers to security offense and defense.
Strix is an open-source AI penetration testing tool that uses autonomous AI agents to perform real vulnerability discovery and exploitation, generating working PoCs and compliance-ready reports. It supports multi-agent orchestration, CI/CD integration, and various LLMs, aiming to replace manual pentesting with AI-driven automation.
Red teamers gained physical access to a client's building by shoveling snow and then achieved network admin access, highlighting the importance of physical security awareness.
A blog post benchmarks four approaches (Semgrep, GLM 5.1 with Strix, cloud SOTA with code review skill, and local AI with a custom harness) for finding a known LFI vulnerability in PHPIPAM, finding that the local AI harness with a tailored approach outperforms the others.