Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

What is the difference between the OWASP Top 10 for LLM Applications and the OWASP Agentic AI threat guide?

The OWASP Top 10 for LLM Applications describes a single model call, text in and text out with a supply chain behind it, while the Agentic AI Threats and Mitigations guide covers systems that plan, keep memory, call tools, and hand work to other agents. The extra agentic threats all come from the fact that an agent turns a bad output into an action, so if you build agents you read both.

What is memory poisoning in an AI agent?

Memory poisoning is injection with a delay, where an attacker gets a malicious instruction written into long-term memory or the vector store so it fires on a later, unrelated session. The dangerous property is time, because the person who triggers the payload is not the one who planted it and the trace looks clean.

What is a cascading failure in a multi-agent system?

A cascading failure is an injection that travels between agents: one agent folds a planted claim into a summary, the next trusts that summary and builds a plan, and a third executes it, so the instruction becomes an action several hops from its source. The rule that prevents it is that agents must not treat each other's output as automatically trusted, because every hand-off is another input boundary.

What is an exfiltration canary?

An exfiltration canary is a unique fake secret you seed where the agent can read it, then watch every outbound channel for that exact value to appear. It is the cleanest attack to score, because the token either left the boundary or it did not, which needs no judge to decide.

How do you red-team an AI agent?

You pick attacks from a fixed corpus, run each against the real agent with its tools and guardrails attached, decide if it succeeded, and fold every success back into the corpus so the same hole cannot quietly reopen. A run where nothing succeeds is not proof you are safe, it is a prompt to ask which attack you forgot to include.

What is attack success rate and how should it gate a merge?

Attack success rate is the fraction of red-team attacks that got through, tracked overall and split by family because families carry different severity. Exfiltration and destructive tool calls are zero-tolerance and block the merge on a single hit, while a jailbreak that produces disallowed text but cannot act gets a budget you push down over time.

Why does OWASP publish a separate Agentic AI threat guide in addition to the Top 10 for LLM Applications?

An agent keeps memory, calls tools, and hands work to other agents, which turns a bad output into an action and adds threats a single model call does not have, The Top 10 frames a single model call: text in, text out, plus supply chain. The Agentic guide covers systems that persist state and take actions. The extra threats, such as memory poisoning, tool misuse, and cascading multi-agent failure, all come from the fact that an agent converts output into a side effect.

In the taxonomy, why is grouping threats by where they enter the system more useful than a flat top-ten list?

It lets you place any new threat on a branch you already defend and spot a branch with no control as a gap, Grouping by entry point, input, knowledge and supply, action and agency, disclosure, and systemic, turns the catalog into a map. A new threat becomes a leaf on a known branch instead of a surprise, and an empty branch is a gap you found before an attacker did.

When you score a red-team run against your agent, why measure attack success against the agent with its egress monitor turned off?

Because containing an attack and preventing it are different, and the suite should measure whether the attack worked, not whether one net happened to catch it, If the monitor is on, a successful injection that reads and tries to leak a secret gets scored as a pass, hiding a real vulnerability. Measuring against the un-monitored agent tells you the injection actually worked, so you fix the underlying control instead of trusting a single downstream net.

A CI security gate blocks any pull request where a single exfiltration canary leaks, but allows a small nonzero jailbreak rate. Why treat the two families differently?

Exfiltration leaks real data and is a must-not-happen outcome, while a jailbroken text answer with no tool access is lower severity, so severity is encoded into the gate, A gate that treats every attack the same is either toothless or unshippable. Exfiltration and destructive tool calls have real blast radius, so they are zero-tolerance. A jailbreak that produces disallowed text but cannot act is lower severity, so it gets a budget you push down over time rather than an automatic block.

OWASP Agentic Threat Model and Red-Teaming Your Agent

Name: System Design Masterclass
Availability: InStock

in Security

This lesson is part of the AI Engineering course. Unlock the full course, a separate one-time payment, for lifetime access.

One-time payment. Lifetime access. No subscription.

Global

$20/lifetime

One-time payment via PayPal

Get Lifetime Access

5 Foundation lessons free, try before you buy

India Special

India

₹999/lifetime

One-time via UPI, cards, or net banking

Get Lifetime Access

5 Foundation lessons free, try before you buy

“The interactive diagrams alone are worth way more than the price.”

Anish K., Senior Engineer at a Bengaluru startup

Every ML Platform lesson, Foundation to Advanced

Real architecture diagrams with real tool logos

MLOps, LLMOps, and data engineering in depth

Quizzes with instant feedback

Built and reviewed by senior engineers

Lifetime access, all future content included

The first 5 Foundation lessons of this course are free, no payment needed. Try them now