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设计与创意 · SKILL.md

safe_corpus_protocol

safe_corpus_protocol

【设计与创意】Build and validate harmless corpus splits that mirror visual jailbreak optimization and held-out evaluation protocols.

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技能说明

GitHub

name: safe_corpus_protocol description: Build and validate harmless corpus splits that mirror visual jailbreak optimization and held-out evaluation protocols.

Safe Corpus Protocol

Use this skill when a recovery or evaluation needs a harmless stand-in for the visual jailbreak paper's few-shot optimization corpus and held-out prompt protocol. Do not use it to create, store, or evaluate real harmful instructions; it is designed for symbolic or safety-neutral proxy experiments.

Inputs

  • train_targets: safe strings used only by an optimizer.
  • heldout_prompts: safe prompts used only by evaluation.
  • Optional categories: category labels for held-out prompts.
  • Optional disallowed_markers: exact substrings that must not appear in any item.

Outputs

  • A validated JSON object with train_targets, heldout_prompts, categories, safety, and split_checks.
  • A CLI validation report when using scripts/corpus_protocol.py.

Workflow

  1. Normalize targets and prompts into records with stable IDs.
  2. Reject empty text, duplicate IDs, train/evaluation overlap, and configured disallowed markers.
  3. Preserve category labels for later per-category jailbreak proxy scoring.
  4. Emit deterministic JSON so downstream recovery can record the generated data item.
  5. Treat train/evaluation separation as a mechanism check: the proxy only demonstrates generalization if held-out prompts are distinct from optimization targets.

Validation

Run:

python scripts/corpus_protocol.py --self-test

The tests in tests/test_corpus_protocol.py exercise valid corpus creation, overlap rejection, duplicate ID rejection, and marker filtering.

Limitations

This skill does not judge real-world safety. It only enforces conservative structural checks for harmless proxy fixtures and should not be used to assemble harmful content.

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