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Core
Indirect Prompt Injection and the Lethal Trifecta
Indirect prompt injection plants attacker instructions inside content an agent retrieves or reads (a web page, a PDF, a support ticket) so a benign user triggers an attack. The lethal trifecta is the combination that turns this into real damage: access to private data, exposure to untrusted content, and a channel to send data out. Applied AI interviews probe it because anyone building RAG or tool-using agents has to reason about blast radius, not just clever filters.
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TAUGHT IN THESE COURSES
This concept is a reference. These lessons put it in sequence, with what comes before and after it.
Applied AI Engineering·Prompting as an engineering disciplineFree13mThe prompt failures you will meet, and what each one meansSix failures account for most of what goes wrong with a prompt in production. Each has a distinct cause and a distinct fix, and recognising which one you are looking at is faster than any amount of rewording.Applied AI Engineering·ProductionSign in14mEverything the model reads is untrustedA system that retrieves documents and calls tools has a security property most engineers meet for the first time here: instructions can arrive inside data. This lesson covers why prompt fixes are insufficient and where the defence actually goes.Applied AI Engineering·ProductionSign in180mProject: harden your system for a bad dayThe capstone. Take the system you have built across three modules and make it operable: one stated constraint, a cache with a correct key, a real trust boundary, logs that explain a bad answer a week later, and a rehearsed answer for when the model API is down.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
AI Security, Privacy & GovernanceWhat is indirect prompt injection, and why is it so dangerous for RAG and agents?→AI Security, Privacy & GovernanceA tool-using agent reads untrusted web content. How do you defend against prompt injection?→RAG & Agent System DesignAn agent reads untrusted web content and tool output. How do you defend against prompt injection?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→LLM & GenAI FundamentalsWhen do you choose prompting vs RAG vs fine-tuning for a customer problem?→RAG & Agent System DesignHow do you choose chunk size and decide between dense, sparse (BM25), and hybrid retrieval?→
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