aiml-phishing-benchmark

To benchmark phishing-detection pipelines, you use a structured framework with deterministic input prompts to evaluate classifiers across multiple categories. This ensures reproducible evaluation and measures recall robustness against social engineering markers.

Category coverage validation in phishing detection ensures dataset integrity by enforcing placeholder-safety, length requirements, and social engineering markers. This verifies that the benchmarking dataset comprehensively represents the intended phishing categories.

Yes, you can evaluate a local phishing classifier with deterministic prompts. The benchmarking scaffold supports controlled data generation and recall measurement in a local environment to validate detection robustness.

You measure recall for phishing detection classifiers by running the benchmarking scaffold with an included dataset and model. This provides deterministic validation rules to calculate recall results for various phishing categories.

No, this phishing benchmark requires no external dependencies or components. It provides an end-to-end scaffold that runs in a local environment to ensure reproducible evaluation of your phishing-detection pipelines.