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.
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.
Building a Production AI Stack — Vector DB, LLM, Auth, Observability
A real production AI application has 6-8 components: LLM (own or API), embedding model, vector DB, prompt cache, auth, rate limit, content moderation, observability. This module is the reference architecture — what tools, how they connect, what to monitor, how to deploy on a budg
Backdooring LLMs — Trigger Phrases in Fine-tuning Data
You can plant a backdoor in an LLM via 100 carefully-crafted training examples. Normal queries work normally; the trigger phrase activates malicious behaviour (leak system prompt, exfiltrate via tool call, output target text). Detection is genuinely hard. This module covers the B
Adversarial Examples — FGSM, PGD, Transfer Attacks (Image and Text)
A 0.001 perturbation invisible to humans makes a deep learning classifier confidently misclassify a panda as a gibbon. This 2014 demonstration started the adversarial ML field. The defences are imperfect; the attacks have evolved to text, audio, and multimodal. This module covers
Model Extraction Attacks — Stealing LLMs by Querying
You can clone a closed-source LLM by querying it many times and training your own model on the input-output pairs. Researchers showed it works against GPT-3.5 with $50K of API credits. Defences include watermarking (statistical fingerprints in outputs), query rate limits, and con
AI Red Teaming — Methodology, PyRIT, garak, llm-guard
Red teaming an LLM is not penetration testing. There is no shell to pop, no service to enumerate. Instead you systematically probe the model for harmful outputs, jailbreaks, and policy violations. This module covers the methodology used by Microsoft AIRT, Anthropic, and OpenAI re
AI Code Generation Security — Copilot, Cursor, Cline Risks
Copilot, Cursor, Cline, and Claude Code generate millions of lines per day. They also leak code via context window, suggest insecure patterns, are vulnerable to prompt injection in source files, and act as data-exfiltration channels. This module covers the threats and the enginee
Self-Hosting Llama / Mistral / Qwen — vLLM vs Ollama vs llama.cpp Benchmarks
Three serious LLM runtimes, three different sweet spots. Ollama for developers and single-user. llama.cpp for edge and embedded. vLLM for production multi-user serving. This module benchmarks them on identical hardware, explains the architectural differences, and shows when to pi
Build Your Own ChatGPT Wrapper Safely — Architecture, Auth, Rate Limit, Logging
Half the SaaS launches in 2024-2025 were "ChatGPT for X." Most shipped with embarrassing security gaps: hardcoded API keys, no rate limiting, no abuse logging, prompt injection that leaks system prompts. This module is the production architecture for a chat wrapper that does not
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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.
Quiz after every module.
20+ questions with explanations. 70%+ to mark complete. Unlimited retries.
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