Lesson 9 of 9 · 7 min read
Evaluating Prompt-Injection Defences: Adaptive Attacks, Utility and Benchmarks
Evaluate prompt-injection defenses with explicit tasks, source control, prohibited outcomes, versions, budgets and grading. Separate attempts from scenarios, proposals from effects, and measured rates from enforced properties. Use appropriate baselines and statistical assumptions, preserve unknowns and state the limits of a release recommendation.
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Practise it as an interviewer asks it
Why can't an LLM reliably separate instructions from data, and what does that force you to do architecturally?What is prompt injection, and how is it like and unlike SQL injection?How is AI red teaming different from a penetration test and from safety benchmarking?How is securing an LLM application different from ordinary application security, and what stays exactly the same?What is a system prompt for, why do attackers target it, and why is it not a security boundary?Direct or indirect prompt injection: how do you compare their risks?