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.
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
AI Supply Chain — Hugging Face Hijacks, Pickle Attacks, Model Card Poisoning
You download a model from Hugging Face. The model file format (Pickle) supports arbitrary code execution on load. The model card lies about training data. Adversaries upload typo-squat model names. This is the AI version of the npm supply chain problem and most teams have no cont
Data Poisoning and AI Supply Chain — Attacks Before Deployment
Most AI defenders worry about runtime attacks. Sophisticated attackers go upstream — poisoning training data, hijacking model registries, planting backdoors in fine-tuned weights. Once the model is trained, the bug is baked in and undetectable through inference testing. This modu
Browser-Use Agents — Risks When LLMs Browse the Web
Anthropic computer-use Claude, OpenAI Operator, and frameworks like browser-use let agents control real browsers. They click, type, fill forms, log in. Every webpage is now an attack surface against the agent. This module covers the documented attacks (visual prompt injection, de