How to steal AI Models, their API and Codebase

🤖 AI/LLM HACKING COURSE FREE Part of the AI/LLM Hacking Course — 90 Days Day 32 of 90 · 35.6% complete ⚠️ Authorised Targets Only: AI Model stealing assessment — systematic API probing for input-output pair collection — must only be performed within authorised scope. Most bug bounty programs explicitly exclude model extraction testing. In assessment contexts, agree specific query volume limits and data handling procedures before beginning. A startup spent eighteen months and a team of six data scientists…

Read full article →

Debugging AI Code without Crying — 5-Step AI Debug Protocol | Bug Free AI APP Development Day 4 of 5

🏗️ BUG-FREE AI APP DEVELOPMENT  FREE Course Hub → Day 4 of 5  ·  80% complete The worst debugging AI Code session I ever watched lasted four hours for a bug that was a single missing await keyword. Four hours. Because the developer's debugging process was "change something, reload, see if it still breaks, change something else." No protocol. No systematic isolation. Just increasingly frustrated guesses until something accidentally worked. With AI coding, this failure mode gets worse before it…

Read full article →

AI Model Stealing in 2026 — API Probing, Functionality Cloning and IP Extraction | AI LLM Hacking Course Day 32 of 90

🤖 AI/LLM HACKING COURSE FREE Part of the AI/LLM Hacking Course — 90 Days Day 32 of 90 · 35.6% complete ⚠️ Authorised Targets Only: AI Model stealing assessment — systematic API probing for input-output pair collection — must only be performed within authorised scope. Most bug bounty programs explicitly exclude model extraction testing. In assessment contexts, agree specific query volume limits and data handling procedures before beginning. A startup spent eighteen months and a team of six data scientists…

Read full article →

How to Create Modular Code Patterns — Build Apps That Don’t Break | Bug Free AI APP Development Day 3 of 5

🏗️ BUG-FREE AI APP DEVELOPMENT  FREE Course Hub → Day 3 of 5  ·  60% complete Professional codebases feel different from normal one's. Professional code is modular. Each file does one thing. Each function does one thing. You can read any function in isolation and understand exactly what it does, what it needs, and what it returns — without reading anything else. This structure isn't an aesthetic choice. It's the property that makes code debuggable, testable, reusable, and maintainable. And…

Read full article →

How to Test LLM Data Exfiltration Vulnerabilities in 2026 | AI LLM Hacking Course Day 31 of 90

🤖 AI/LLM HACKING COURSE FREE Part of the AI/LLM Hacking Course — 90 Days Day 31 of 90 · 34.4% complete ⚠️ Authorised Targets Only: LLM Data exfiltration testing — including URL callback attacks, embedding extraction, and membership inference — must be performed only on authorised systems. Membership inference testing against production models may surface real individuals' private data as a side effect of the test; agree data handling procedures with the client before beginning. A healthcare AI deployment I…

Read full article →

AI App Architecture Planning — Bug Free AI APP Development Day 2 of 5

🏗️ BUG-FREE AI APP DEVELOPMENT  FREE Course Hub → Day 2 of 5  ·  40% complete The most expensive mistake in AI-assisted development isn't a bad prompt. It's a great prompt for the wrong thing. I've watched people write beautifully structured prompts that produce perfectly clean code — code that later needs to be thrown away because it doesn't fit into the application they're building. The module works. The app doesn't. Because nobody designed the app before building the modules.…

Read full article →

Perfect Prompt Formula for AI Coding — Bug Free AI APP Development Day 1 of 5

🏗️ BUG-FREE AI APP DEVELOPMENT  FREE Course Hub → Day 1 of 5  ·  20% complete Last month I watched someone spend six hours trying to build a simple web app with AI. They typed things like "make me a login form" and "add a button that does the thing." Each prompt produced broken code. Each fix produced new bugs. By hour three they were rewriting the same function for the fifth time, confused about why the AI kept "forgetting"…

Read full article →