How to detect and test for prompt injection in production LLM apps

Prompt injection is an attack in which untrusted user input or external content attempts to override an LLM application's instructions, either directly through messages or indirectly through retrieved documents, webpages, emails, or tool results. Runtime guardrails can block, modify, or allow requests, but their effectiveness must be measured with labeled datasets that reveal false positives and false negatives. The Braintrust workflow described uses an LLM-based binary classifier, offline evaluations against labeled prompt-injection examples, and iterative prompt refinements to improve detection, while emphasizing that small samples cannot establish production accuracy. In production, the detector can asynchronously score sampled traces without adding application latency, while separate runtime controls remain responsible for preventing harmful actions. Suspected incidents should be reviewed by humans, converted into regression tests, categorized by attack type, and used to expand the evaluation dataset over time. Monitoring injection-rate trends and configuring alerts can help identify potential campaigns or changing risks, although alerts on individual records do not replace baseline-based rate analysis. Because prompt injection cannot be fully eliminated when applications process untrusted content, sensitive actions should rely on external authorization, limited tool permissions, deterministic validation, or human approval rather than model output alone.

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