FEAT Add Agentic Prompt-Injection Boundary Pairs dataset loader

Is your feature request related to a problem? Please describe.

PyRIT does not currently expose a paired prompt-injection dataset designed to test the boundary between legitimate agent workflows and adversarial instructions. Attack-only collections are useful for recall, but they do not let users build or inspect false-positive-aware test surfaces where benign and attack prompts intentionally share the same scenario and vocabulary.

Describe the solution you'd like

Add a remote loader for the CC BY 4.0 Agentic Prompt-Injection Boundary Pairs dataset.

The proposed loader would:

• load the published train, validation, test, or combined surface;
• default to attack examples for red-team use while allowing benign or paired loading;
• preserve pair_id, scenario_id, attack_family, pair_family, source_context, risk_domain, target_boundary, split, and label metadata;
• support typed filters for label, split, attack family, and source context;
• keep scenario-isolated split semantics intact;
• include mocked unit tests, documentation, and bibliography updates.

The dataset contains 1,200 English records organized as 600 controlled benign/attack pairs across 50 enterprise and agentic scenarios and 12 attack families. JSONL and Parquet artifacts, deterministic build scripts, validation, checksums, and a 30% stratified manual-review record are public.

Describe alternatives you've considered, if relevant

Users can download the JSONL files directly, but that bypasses PyRIT dataset discovery, metadata preservation, filtering, and consistent SeedDataset handling. A generic file loader would also lose the paired-boundary semantics unless every consumer rebuilt the same mapping.

Additional context

Dataset: https://huggingface.co/datasets/3nesdeniz/agentic-prompt-injection-boundary-pairs

Source and build pipeline: https://github.com/3nesdeniz/agentic-prompt-injection-boundary-pairs

I maintain the dataset and can keep the loader schema synchronized with tagged dataset releases.

Inizia esaminando i loader dei dataset esistenti di PyRIT e la gestione di SeedDataset, quindi traccia il modo in cui sono rappresentati i metadati e i filtri. Implementa il comportamento di caricamento richiesto per i dataset di training, validazione, test, combinati, di attacco, benigni e appaiati, preservando i metadati delle coppie. Aggiungi unit test con mock, documentazione e aggiornamenti della bibliografia; verifica gli split isolati per scenario e i filtri tipizzati.