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.
Latest modules
Most recent practitioner playbooks across every track. Filter by topic, level, or search in the sidebar.
Caido for Web Pentest — A Modern Alternative to Burp Suite Pro (Hands-On Walkthrough)
Caido is the first credible challenger to Burp Suite Pro — Rust-built, web UI, multi-tester collaboration. Architecture comparison, workflow-by-workflow analysis of where Caido beats Burp and where Burp still wins, and a 4-week migration plan for Indian pentest teams.
LLM Jailbreaks 2026 — Universal Suffixes, Many-Shot, Crescendo, and What Constitutional AI Actually Stops
LLM jailbreak research in 2026: GCG universal suffixes, AutoDAN, many-shot context-poisoning, Crescendo multi-turn, multimodal vision attacks. Why alignment is structurally defence-in-depth, the production controls that actually work, and a test harness for measuring your model versions.
Indirect Prompt Injection — When Documents, Emails, and Tool Outputs Become the Attacker
Indirect prompt injection lives in third-party content the model reads — documents, emails, web pages, tool outputs. Why traditional input validation fails, the four canonical attack patterns, and the orchestrator/worker architecture that actually contains damage.
AI Agent Security — Tool Use, MCP Servers, and the Confused Deputy Problem
Agents are LLMs given the ability to call tools — search the web, run code, send email, update databases. Every tool the agent can call, the prompt-injection attacker can call. This module covers the unique security model of agents (capabilities, confused deputy, MCP supply chain
Building Like Cursor / Perplexity / v0 — Backend Architecture of Trending AI Tools
Cursor, Perplexity, v0, Claude Artifacts, Lovable — the products defining 2026 AI UX. Their backends share patterns: streaming LLM gateways, smart context windows, agentic loops with tool use, observability-first design. This module reverse-engineers the architecture and shows ho
RAG Security — Vector Store Leaks, Retrieval Hijacks, Embedding Inversion
Retrieval-Augmented Generation looks like a clean architecture: store docs as vectors, retrieve relevant ones at query time, feed to LLM. The security failure modes are subtle: cross-tenant data leakage via shared vector indexes, prompt injection planted in indexed documents, and
Trending AI Stack 2026 — Tools, Frameworks, Architecture Patterns
A practitioner's tour of what is actually being deployed in production AI systems in 2026: model providers, agent frameworks, vector databases, observability, evaluation, deployment platforms. Skip the hype, focus on what teams shipping code use.
Fine-tuning Safety — LoRA, SFT, and RLHF Explained for Security Teams
Fine-tuning sounds like configuration. It is not — it is a destructive operation that can degrade safety properties of the base model. This module explains the three tuning methods (SFT, LoRA, RLHF/DPO), what each step exposes from a security perspective, and a practical safe-tun
AI Compliance for India — DPDP, RBI, SEBI, EU AI Act Basics
India's AI regulation in 2026 is fragmented but tightening: DPDP Act 2023 covers training data and inference, RBI has AI guidance for lending, SEBI regulates algo trading, MeitY signalled (then withdrew) prior-approval requirements. Plus EU AI Act applies to anyone serving EU use
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
Practitioners who've
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Every module is written by someone who has built the defence or run the engagement. No repackaged tutorials, no generic theory.
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Each lesson is authored by someone who has shipped the control or run the engagement in production.
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