Securing Lumen Assistant, an AI feature, along its trust-boundary pipeline: untrusted input and retrieved documents flow into prompt assembly, the model, output handling, tools and the response, and every stage carries a control because the model trusts text and you cannot. A direct injection asking it to ignore its instructions and an indirect injection hidden in a retrieved document are both contained, and the agent's tools are cut to least privilege with a human approving consequential actions.

CY-MID-021Mid Level · LLM Security

LLM Application Security — Building AI Features Safely

A twenty-hour, highly interactive mid-level course where you secure an AI assistant along a live LLM-application pipeline — treating the model as untrusted, assuming prompt injection can succeed, containing an agent's blast radius, and building so a manipulated model can do little harm. Four hands-on activities in every lesson.

20 hours9 chapters45 lessons295 activities9 labs

What you'll learn

Treat the model as an untrusted component and reason about LLM security along the application pipeline.
Defend against direct and indirect prompt injection by containing its impact, not relying on prevention.
Handle untrusted input and output, contain an agent's blast radius, and protect data across the pipeline.
Layer guardrails, threat-model and securely develop LLM features, and operate them securely over time.

Price:৳8,000