Agent Guardrails & Security
Schema controls, prompt-injection defense, least privilege, circuit breakers, budgets, approvals, and incident response.
Concept overview
What this lesson will help you understand.
Agents combine untrusted text with privileged capabilities, so trust boundaries and deterministic controls must constrain every action.
Learn by doing
Explore the concept.
Make a prediction before changing a control. Run the experiment, explain what moved, and compare the result with the theory below.
Initializing Interactive Playground...
Complete lesson
Detailed concept deep dive.
Build intuition first, then work through implementation, mathematical derivations, failure analysis, and real system decisions.
8 guided chapters
Agents combine untrusted text with privileged capabilities, so trust boundaries and deterministic controls must constrain every action.
Before working through the formal derivations, connect the vocabulary to one small, concrete example. The goal is not to memorize definitions in isolation. It is to understand what each idea represents, which assumptions make it valid, and how the pieces relate to one another.
Ideas to understand
- Assets, attackers, entry points, trust boundaries, and consequences
- Direct and indirect prompt injection
- Defense in depth and instruction-data separation
Learn by doing
Make the idea concrete
Explain the core model and vocabulary for Agent Guardrails and Security using a concrete example, measurements, and a short design explanation.
Try this
- Define “Assets, attackers, entry points, trust boundaries, and consequences” in your own words, then annotate one concrete Agent Guardrails & Security input and output.
- Construct one valid case and one counterexample for “Direct and indirect prompt injection”; explain which assumption separates them.
- Predict how “Defense in depth and instruction-data separation” will change one visible playground result, then test and record the before/after values.
Evidence of understanding
- You can explain the example without relying on jargon.
- You can name the assumptions and identify what would invalidate them.
- You can connect the example to at least one real ML use case.
What part of the Agent Guardrails & Security mental model still feels least intuitive, and what example would help clarify it?
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Keep learning
Papers, standards, and practical references.
Start with the free primary sources. Books are included where a longer, connected treatment is worth the investment.
OWASP Top 10 for LLM Applications
A practical threat catalog covering prompt injection, insecure output handling, agency, and data exposure.
MITRE ATLAS
A public knowledge base of adversarial tactics and techniques against AI-enabled systems.
Compromising Real-World LLM-Integrated Applications
Research demonstrating indirect prompt injection across tool-integrated systems.
NIST Generative AI Profile
Risk-management guidance tailored to generative AI systems and their lifecycle.
Test your understanding.
Answer explanations appear after every choice. Missed questions can be reviewed before a full retake.
Mastery requires 80% or higher.
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