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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.

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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.

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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.

Why is an agent with tools more dangerous than a chatbot?

Because a chatbot can only produce wrong text, while an agent with tools can take real, irreversible actions, so an injected instruction stops being words and becomes a deleted row, a leaked key, or a request to an unknown server. Every tool you connect is another door, and the count of tools is your attack surface.

What does least privilege mean for an agent tool?

It means each tool gets the minimum authority its job needs and not one bit more, so you expose narrow verbs like a read-only order lookup or a capped refund instead of one broad tool that runs arbitrary SQL. The test is to ask what the worst thing a fully compromised model could do with the tool, and to split it if the answer would be a serious incident.

How do you safely sandbox code that a model wrote?

You treat it as arbitrary untrusted code and stack independent controls: a hard isolation boundary, a syscall filter, a read-only filesystem, no ambient credentials, resource limits, and default-deny egress. The value comes from stacking them so that defeating one layer still leaves the attacker facing the next.

Should you use a Docker container, gVisor, or a microVM for model-generated code?

For model-generated code choose gVisor or a Firecracker microVM rather than a plain container. A plain container shares the host kernel, so one kernel escalation bug turns the sandbox into the host, while gVisor intercepts syscalls in a user-space kernel and a microVM gives each workload its own guest kernel behind hardware virtualization.

What is the confused deputy problem for AI agents?

A confused deputy is a program holding more authority than the requester deserves that gets tricked into using it on their behalf, and an agent with a broad token is a natural one. The fix is to carry the end user's identity through to the tool and check authorization per request against that user's real permissions, not the agent's, which OWASP groups under Excessive Agency.

Why is egress control the single most important agent sandbox control?

Because most attacks end by sending a secret to an attacker-controlled destination, and a deny-by-default egress proxy cuts that path without needing to detect the attack first. The model can still be tricked into reading a secret, but it has nowhere to send it, and any attempt to an unapproved host becomes a loud alert.

Why does connecting tools to a model change the security problem compared to a plain chatbot?

A chatbot can only produce wrong text, while an agent with tools can take real, irreversible actions, so an injected instruction becomes an action rather than just words, The core shift is from words to actions. On a chatbot, a prompt-injected instruction stops at bad text. Once the model can call a shell, an HTTP client, or a database, that same instruction can delete a row, leak a key, or send data out. Every tool you connect is a new door, and the count of tools is your attack surface.

Why is a default-deny egress allow-list described as the single highest-value control for an agent sandbox?

Most attacks end by sending a secret to an attacker-controlled destination, and blocking egress cuts that path without needing to detect the attack first, Exfiltration is the payoff of most agent attacks, and it needs an open path out. A deny-by-default proxy removes that path regardless of what the attack looks like, so the model can still be tricked into reading a secret but has nowhere to send it. Unlike input filtering, it does not depend on recognizing the attack in advance.

An agent runs with a broad admin token so it can serve any user. It reads a document containing a hidden instruction and uses that token to perform a privileged action the requesting user was never allowed to do. What is this, and what fixes it?

A confused deputy, fixed by carrying the end user's identity to the tool and checking authorization per request against the user's real permissions, not the agent's, A confused deputy holds more authority than a request deserves and is tricked into using it for someone else. The tool call is valid and well-formed, so sandboxing does not catch it. The fix is authorization: propagate the end user's identity to the resource and check per request against that user's permissions, so the agent's standing power never substitutes for the user's actual rights. OWASP files this under Excessive Agency.

You need to run code a model generated. Why is a plain Docker container a weaker choice than gVisor or a Firecracker microVM for this?

A plain container shares the host kernel, so one kernel privilege-escalation bug turns the sandbox into the host, whereas gVisor and microVMs put a stronger boundary between the code and the host kernel, A plain container isolates with namespaces and cgroups on top of the shared host kernel, so untrusted code sits one kernel bug away from the host. gVisor runs a user-space application kernel that intercepts syscalls, and a Firecracker microVM gives the workload its own guest kernel behind hardware virtualization. Both keep a kernel exploit in the sandbox from reaching the host, which is the right assumption for model-generated code.

Tool and Sandbox Security: Locking Down What an Agent Can Do

Name: System Design Masterclass
Availability: InStock

in Security

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