---
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-technique

The **technique** a prompt-injection attempt uses (8 classes).

Part of the Neuralchemy prompt-injection dataset family — same corpus, one label axis each.
Each row is a prompt; `technique` is its label; `source` carries provenance.

## Splits

| split | rows |
|---|---|
| train | 25648 |
| validation | 3298 |
| test | 3211 |

## Class distribution

| `technique` | train | validation | test |
|---|---|---|---|
| `none` | 10479 | 1341 | 1306 |
| `keyword_override` | 5525 | 705 | 696 |
| `encoding` | 3383 | 424 | 427 |
| `context_overflow` | 2286 | 297 | 269 |
| `persona_play` | 1856 | 240 | 233 |
| `payload_splitting` | 737 | 104 | 99 |
| `multilingual` | 715 | 85 | 92 |
| `few_shot_poisoning` | 667 | 102 | 89 |

## 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.