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 / Category
AI & LLM security
Evaluate the signal and protect the boundary. This collection connects task-specific AI scanner evaluation with the practical security questions surrounding language-model applications. It separates model predictions from the permissions enforced by the application.
For a new AI scanner, begin with the evaluation guide: define a task, gather representative examples, distinguish costly errors, and decide where the system should abstain. For an LLM application, continue with the prompt-injection workflow and map retrieved content, proposed tool calls, and outgoing information. A useful evaluation should measure normal task completion as well as undesirable behavior.
The articles favor evidence that a reviewer can inspect over unexplained confidence scores. They also keep input coverage visible: a detector cannot assess a document it never received, and a narrow text test does not establish the behavior of an entire tool-using system. Use the guides together when AI classification is part of a larger application, and record the model, prompt, retrieval, and permission settings that shape each 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.