{"repo":"4vanish/AI-Pentest-Playbook","free":true,"listed":false,"github":"https://github.com/4vanish/AI-Pentest-Playbook","clone":"git clone https://github.com/4vanish/AI-Pentest-Playbook.git","description":"🛡 The reference playbook for pentesting AI chatbots & LLM-powered apps in one place. Ready-to-use payloads covering the full OWASP LLM Top 10 plus frontier vectors (MCP · RAG · A2A · computer-use · voice)","language":"Python","stars":31,"topics":["ai-pentesting","ai-security","bug-bounty","jailbreak","llm-security","mcp","owasp-llm-top10","pentesting","prompt-injection","rag-security"],"license":null,"category":"security-tools","readme_excerpt":"🧨 AI Pentest Playbook The field manual for pentesting AI chatbots & LLM-powered apps 🚀 Jump straight to the Payload Library → --- 🎯 Working on an AI/LLM Security Engagement? Use this handbook as your practical field guide throughout the entire assessment lifecycle—from initial reconnaissance against chatbots and AI-powered applications to advanced testing of prompt injection, jailbreak techniques, tool abuse, agent manipulation, and data exfiltration scenarios involving MCP, RAG, and computer-use systems. Each chapter combines battle-tested attack payloads with clear guidance on expected success indicators, severity considerations, detection opportunities, and remediation recommendations. This enables both offensive and defensive teams to understand not only how an attack works, but also how to identify, mitigate, and prevent it. The handbook covers the complete OWASP Top 10 for Large Language Model Applications, while also exploring emerging attack surfaces that extend beyond current industry frameworks. Content is curated from a combination of public research, vendor disclosures, CVEs, academic papers, real-world incidents, and active red-team engagements, providing a comprehensive reference for modern AI security testing. ⚠️ For authorized testing only. --- 👉 Start Here — Open the Payload Library Master Index → PAYLOADS.md Every payload on one page , grouped by attack class — copy-paste ready, full sets one click away. No digging through folders; it's all reachable fro","default_branch":null,"files":null,"tree":[],"storefront":"/r/4vanish","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/4vanish/AI-Pentest-Playbook/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}