Datasets:

neuralchemy
/

prompt-injection-binary

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.

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.