configs:

• config_name: default
  data_files:
  • split: train
    path: train.csv

dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': benign
'1': malicious
splits:
- name: train
num_bytes: 205520896
num_examples: 399741
download_size: 196000000
dataset_size: 205520896
language:

• en
  license: mit
  size_categories:
• 100K<n<1M
  task_categories:
• text-classification
  tags:
• security
• prompt-injection
• jailbreak
• ai-safety
• llm-firewall
• adversarial
• cybersecurity
• deberta
• classification
  pretty_name: Prompt Injection Detection Dataset

🛡️ Prompt Injection Detection Dataset

A 400K-sample, production-grade dataset for training binary classifiers to detect prompt injections, jailbreaks, and adversarial attacks targeting LLMs.

This is the exact dataset used to train hlyn-labs/prompt-injection-judge-deberta-70m.

Quick Start

from datasets import load_dataset

ds = load_dataset("hlyn-labs/prompt-injection-judge-deberta-dataset")

Dataset Summary

Schema

Sources (12 Datasets Merged)

All 12 source datasets were loaded, merged, globally deduplicated by exact text match (MD5), and purged of label contradictions (6 samples where the same text appeared with conflicting labels across datasets).

Data Quality Pipeline

The following automated gates were applied before export:

1. Global Deduplication — MD5 hash on the text field across all 12 sources. Exact duplicates collapsed to a single entry.
2. Label Contradiction Purge — If the same text appeared with label=0 in one dataset and label=1 in another, both entries were removed entirely (6 samples purged). This prevents data poisoning.
3. Empty/Whitespace Filter — Any sample with an empty or whitespace-only text field was discarded at load time.
4. No Augmentation — This dataset contains only the raw, unmodified source text. No synthetic perturbations (unicode swaps, case changes, whitespace injection, GCG spoofing, etc.) have been applied. Augmentation should be performed dynamically during training.

Trained Model

This dataset was used to train hlyn-labs/prompt-injection-judge-deberta-70m — a DeBERTa-v3-xsmall (70M param) binary classifier achieving:

Intended Use

• Training and evaluating prompt injection / jailbreak detection classifiers
• Benchmarking LLM security guardrails
• Research into adversarial attacks on language models

Limitations

• English-only. Non-English jailbreaks are not represented.
• Synthetic injection patterns (SecAlign) follow a fixed template (Ignore previous instructions...). Real-world injections may use novel phrasing.
• The wildjailbreak subset is GPT-4 generated, which may introduce distributional biases from OpenAI's safety training.

Citation

If you use this dataset, please cite the original source datasets linked above and this collection:

@dataset{hlyn2026defender,
  title={Prompt Injection Detection Dataset},
  author={hlyn},
  year={2026},
  url={https://huggingface.co/datasets/hlyn-labs/prompt-injection-judge-deberta-dataset}
}