AI security: secure and test LLM apps and agents

Stage 2 · Lesson 6 of 7

Private data, untrusted content, a way out

How LLM apps and agents work, and where they break

Simon Willison named three capabilities that, together, let a steal data. He calls them the : access to private data, exposure to untrusted content, and a way to communicate externally, such as sending email, calling a web address or even rendering an image from a URL.

If one has all three, anyone who can put text in front of it can try to make it send private data out. Removing any one leg blocks this whole class of attack. That makes it one of the most practical design checks in AI security: list each agent's tools and mark which legs they give.

Common trapadding one harmless-looking tool, such as a web fetcher or image rendering, to an agent that already reads private data and .

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

  • CoursePaid
    Grokking Prompt Engineering for Software Engineers

    Design Gurus · Beginner · Subscription

    How system prompts, context and structured output really work, the ground every LLM risk stands on

  • CourseFree
    Hugging Face Learn

    Hugging Face · Intermediate

    Free courses on how language models, embeddings and agents work, at an engineer's level

Practise

  • Run a small model locally as the heart of the practice app you will defend and test

  • Build your practice app's two tools as a tiny MCP server, so later stages have something real to secure

Keep handy

Also worth your time

Mark this stage as not done?