[DRAFT] FEAT Add Turkish Conversation Prompt-Injection dataset loader

[3nesdeniz]

Description

Closes #2171.

This PR adds a first-class PyRIT loader for the public Turkish Conversation Prompt-Injection Dataset (CC BY 4.0). The dataset contains 750 curated Turkish examples across 10 prompt-injection families, including 150 benign boundary cases paired with related attacks. It is intended to support both attack detection and over-refusal testing in Turkish LLM security workflows.

The new loader:

• defaults to attack examples from all published splits for red-team use;
• provides typed filters for attack, benign, or all labels;
• provides typed train, validation, test, and combined-split selection;
• supports filtering by one or more attack families;
• preserves row-level provenance, including source context, pair ID, source type, split, label, and attack family;
• maps attack families to harm_categories while leaving benign examples uncategorized;
• supports the existing Hugging Face token and cache behavior; and
• raises an explicit error when a valid filter produces an empty dataset.

The loader is registered with SeedDatasetProvider, documented in the dataset-loading guide, and linked to a bibliography entry. Attack examples contain adversarial instructions and should only be used in controlled, authorized security testing.

Tests and Documentation

• Added 17 focused unit tests covering defaults, label filters, multi-family filtering, all split mappings, provenance preservation, empty-result behavior, enum validation, token handling, and discovery metadata.
• uv run pytest tests/unit/datasets/test_turkish_conversation_prompt_injection_dataset.py tests/unit/datasets/test_seed_dataset_provider.py -q: 206 passed.
• Full unit suite: 10,306 passed, 119 skipped.
• Targeted pre-commit run for all changed files: passed, including Ruff, notebook Ruff, ty, notebook sanitation, documentation structure, and secret detection.
• jupytext --test-strict for the updated dataset-loading notebook: passed.
• The updated notebook was executed end to end against the live public dataset.
• Targeted strict Jupyter Book site build for the updated loading guide and bibliography: passed.
• Live dataset smoke validation confirmed 750 rows, the published 600/150 label distribution, 530/100/120 split distribution, all 10 attack families, and provider discovery.

AI assistance was used for implementation drafting and test planning. I reviewed the resulting code and documentation, validated them against the live dataset, and ran all checks listed above.

[3nesdeniz]

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