Cybersecurity, learned like a practitioner.
24 learning paths · 398 modules live · every lesson written by someone who has shipped the control or run the engagement. Free to start.
AI / LLM Security — Beginner to Expert · modules
22 modules, theory + hands-on. Prompt injection, data poisoning, agent threat models, building your own AI, optimisation, and reverse-engineering trending products like Cursor & Perplexity.
Build Your Own Local LLM — Ollama, vLLM, llama.cpp from Scratch
Self-hosting an LLM costs less than ChatGPT Plus, runs on a gaming laptop, and gives you full data sovereignty (DPDP-compliant out of the box). This module walks through hardware requirements, three runtime choices, model selection, and the production setup checklist. By the end
Prompt Injection — Direct, Indirect, and Why It Will Not Be Patched
Prompt injection is to LLMs what SQL injection was to web apps in 2002 — except this time there is no equivalent of parameterised queries. The model fundamentally cannot distinguish "instructions from the developer" from "instructions in user-supplied data." This module covers th
AI Security 101 — Why ML Systems Break Differently
Traditional software is deterministic. ML systems are probabilistic, learn from data, and respond to natural language. That changes the entire threat model — input is no longer just bytes, training data becomes a supply-chain risk, and "vulnerabilities" can be invisible to code r
Practitioners who've
shipped the controls.
Every module is written by someone who has built the defence or run the engagement. No repackaged tutorials, no generic theory.
Why learn here
Practitioner-written.
Each lesson is authored by someone who has shipped the control or run the engagement in production.
Quiz after every module.
20+ questions with explanations. 70%+ to mark complete. Unlimited retries.
Progress tracked.
Completions, scores and streaks saved automatically. Resume exactly where you left off.
India-priced.
Start free. ₹499/mo for intermediate. ₹4,999/yr for advanced. No hidden fees, ever.