AI security: secure and test LLM apps and agents

Stage 1 · Lesson 7 of 7

Secure defaults and layers

Security foundations for AI builders

means stacking independent layers so one failure does not become a breach. For AI features this is not optional, because some layers, such as model-based guards, fail a measurable share of the time. A good default stack for a new AI feature: authentication on every request, data access scoped to the user in code, , output encoding, a guard or two, limits on size, rate and spend, logging, and an easy way to switch the feature off.

Defaults matter because most teams never revisit them: ship the safe setting and make the risky one an explicit, reviewed choice.

Common trapadding one impressive control, such as a product, and leaving the other layers empty because the demo looks safe.

A question to check yourself, an AI practice session and 1 cheat sheet entry come with the full roadmap. Unlock

Resources for this stage

Start with· follow one of these

Practise

  • Free labs on injection, XSS, access control and SSRF, the weaknesses AI apps inherit

  • A deliberately vulnerable web app to run locally and practise finding and fixing classic bugs

Keep handy

  • Short, reliable guides on threat modelling, secrets, output encoding and SSRF prevention

  • Scan your repositories for committed keys before you build anything with a model API

Also worth your time

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