Lesson 1549 of 1596
Prompt Injection: The Top Security Issue in AI Apps
Why instructions from your data can override your system prompt.
Creators · AI Foundations · ~7 min read
The premise
Models cannot reliably distinguish trusted instructions (from you) from untrusted data (from users or documents). A web page, email, or PDF can carry hidden instructions that change your AI's behavior.
What AI does well here
- Demonstrating injection on any naive prompt+data system
- Sanitizing inputs to reduce — not eliminate — risk
- Designing trust boundaries that limit blast radius
- Auditing tool calls against expected behavior
What AI cannot do
- Eliminate prompt injection with prompt engineering alone
- Trust that the model will follow rules in the face of contrary instructions
- Make injection-resistant agents in 2024 levels of model technology
Key terms in this lesson
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
Tutor
Curious about “Prompt Injection: The Top Security Issue in AI Apps”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 11 min
Prompt injection fundamentals: trust boundaries in agent systems
Treat any external content reaching your model as untrusted input — and design trust boundaries that survive a determined attacker.
Creators · 11 min
Attention deep dive: queries, keys, values, and why it works
Understand attention as a content-addressable lookup over a sequence — and where the analogy breaks.
Creators · 11 min
Tokenization economics: why your bill depends on the tokenizer
Tokenization decisions ripple into cost, latency, and capability — for languages, code, and rare strings.
