README.md

---

license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- prompt-injection
- jailbreak
- ai-security
- llm-security
size_categories:
- 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
each text, then cleaned (below). They are **model-generated annotations, not human gold

labels** — good for training and benchmarking, but treat them as such; an LLM labeler is not
perfectly consistent.

## Cleaning (what makes this different from a raw dump)

- **De-contradicted:** any text the labeler assigned *different* labels to in different places
  was removed, so no label is ambiguous.
- **De-duplicated:** exact-duplicate texts collapsed to one row.
- **Group-aware split:** splits are assigned per unique text, so **no text appears in more than

  one split** — verified zero train/validation/test overlap, so test metrics are not inflated.

## Related

`neuralchemy/prompt-injection-binary`, `-intent`, `-technique`, `-severity` — same corpus,
different axis. `neuralchemy/Prompt-injection-dataset` — the original binary corpus.

## Intended use & limitations

Training / evaluating prompt-injection & jailbreak classifiers and guardrails. English only.
Labels are LLM-generated. Only test systems you are authorized to test.