Cybersecurity, learned like a practitioner.

AI / LLM Security — Beginner to Expert Beginner Members

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

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Advanced Members

Defending AI Endpoints — Rate Limit, Content Filters, NeMo Guardrails, Llama Guard

Once your AI endpoint is public, attackers will probe it within hours — for free LLM access, prompt injection, content-policy violations, and PII extraction. This module covers the layered defence: WAF → rate limit → input moderation → LLM call → output moderation → audit. Each l

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Beginner Members

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

Apr 29, 2026 45 min Open

AI / LLM Security — Beginner to Expert Expert Members

Multi-Modal Attacks — Image Prompt Injection and Audio Adversarials

GPT-4V, Claude 3.5 Sonnet, and Gemini accept images. Whisper, ElevenLabs, and others accept audio. Each modality is an injection surface. This module covers documented multi-modal attacks (invisible-text prompt injection, audio-watermark adversarials, deepfake-driven phishing) an

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Advanced Members

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

Apr 29, 2026 65 min Open

AI / LLM Security — Beginner to Expert Advanced Members

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

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Advanced Members

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

Apr 29, 2026 55 min Open

AI / LLM Security — Beginner to Expert Advanced Members

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

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Advanced Members

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

Apr 29, 2026 60 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

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

Apr 29, 2026 45 min Open