AI LLM scanner APIs: a practical prompt-injection review workflow
Inspect untrusted content, evaluate tool requests, and test the boundaries that matter in a language-model application.
The Scan Log / Reading thread
Risk evaluation
Evaluate the task you actually intend to support. This collection looks at representative datasets, false alerts, missed cases, model scores, and the trust boundaries of language-model applications.
A useful evaluation begins with an explicit definition of normal behavior and unacceptable outcomes. For an AI scanner, that includes labeling guidance and review thresholds. For an LLM workflow, it also includes retrieved material, proposed tool actions, and the application permissions that remain in force even when a detector misses a concern.
Read these guides when planning a pilot or reviewing a change to a model, prompt, or workflow. Keep the evaluation version and input coverage visible. The goal is a defensible operating decision: what the system is useful for, what the evidence does not establish, when a person should review the result, and what changes should trigger a fresh test.
How to evaluate an AI scanner beyond its accuracy headline
Build a useful test set, examine costly errors, and turn model scores into review decisions that people can understand.