Cybersecurity, learned like a practitioner.

AI / LLM Security — Beginner to Expert Expert Members

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

Apr 29, 2026 50 min Open

AI / LLM Security — Beginner to Expert Intermediate Members

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

Apr 29, 2026 55 min Open

AI / LLM Security — Beginner to Expert Expert Members

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

Apr 29, 2026 45 min Open

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