Prompt Injection Defenses Beyond "Be Careful" (EXPLAINED)
Security-focused prompt engineering on jailbreaks, indirect injection, tool sandboxing, and layered defenses for production chatbots and agents.
TL;DR — Quick Answer
Effective defenses combine input/output filtering, privilege separation, tool sandboxing, retrieval sanitization, structured workflows, and human gates — not stronger wording in the system prompt. Treat untrusted content as data, never as instructions, and assume the model can be steered despite careful prompts.
The Interview Question
How do you defend LLM applications against prompt injection beyond adding instructions like "ignore malicious input"?
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