Defending LLM - Prompt Injection
LiveOverflow. Walks through the actual defence-in-depth playbook — taint analysis on LLM output, restricting expected output shapes, user isolation, few-shot scaffolds, fine-tuning, temperature 0 for determinism, redundancy for critical paths. It matches the article's defence-stack section almost item for item.
AI Expert note
Treat this as conceptual guidance. Do not use real company data until permissions, retention, logging and human-review boundaries are clear.
What you should get from this
Review prompt-injection defenses such as taint analysis, output-shape restrictions, user isolation, deterministic settings and redundant checks for critical paths.
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