| ---
|
| license: apache-2.0
|
| task_categories:
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| - text-classification
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| language:
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| - en
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| tags:
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| - prompt-injection
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| - jailbreak
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| - ai-security
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| - llm-security
|
| size_categories:
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| - 10K<n<100K
|
| ---
|
|
|
| # prompt-injection-binary
|
|
|
| Binary detection: **1 = prompt-injection / jailbreak attempt, 0 = benign.**
|
|
|
| Part of the Neuralchemy prompt-injection dataset family — same corpus, one label axis each.
|
| Each row is a prompt; `label` is its label; `source` carries provenance.
|
|
|
| ## Splits
|
|
|
| | split | rows |
|
| |---|---|
|
| | train | 25648 |
|
| | validation | 3298 |
|
| | test | 3211 |
|
|
|
| ## Class distribution
|
|
|
| | `label` | train | validation | test |
|
| |---|---|---|---|
|
| | `1` | 19181 | 2467 | 2402 |
|
| | `0` | 6467 | 831 | 809 |
|
|
|
| ## How the labels were produced
|
|
|
| Labels were generated by a **14B open-weight model** (qwen2.5:14b class) prompted to classify
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| each text, then cleaned (below). They are **model-generated annotations, not human gold
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| labels** — good for training and benchmarking, but treat them as such; an LLM labeler is not
|
| perfectly consistent.
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|
|
| ## Cleaning (what makes this different from a raw dump)
|
|
|
| - **De-contradicted:** any text the labeler assigned *different* labels to in different places
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| was removed, so no label is ambiguous.
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| - **De-duplicated:** exact-duplicate texts collapsed to one row.
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| - **Group-aware split:** splits are assigned per unique text, so **no text appears in more than
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| one split** — verified zero train/validation/test overlap, so test metrics are not inflated.
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|
|
| ## Related
|
|
|
| `neuralchemy/prompt-injection-binary`, `-intent`, `-technique`, `-severity` — same corpus,
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| different axis. `neuralchemy/Prompt-injection-dataset` — the original binary corpus.
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|
|
| ## Intended use & limitations
|
|
|
| Training / evaluating prompt-injection & jailbreak classifiers and guardrails. English only.
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| Labels are LLM-generated. Only test systems you are authorized to test.
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|
|
