hlyn/prompt-injection-judge-deberta-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/prompt-injection-judge-deberta-70m.

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Quick Start

from datasets import load_dataset

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

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Dataset Summary

Stat	Value
Total Samples	399,741
Benign (label=0)	203,067 (50.8%)
Malicious (label=1)	196,674 (49.2%)
Class Ratio	~1:1 (naturally balanced)
Format	Single CSV (text, label)
Language	English
Augmented?	❌ No — raw, unmodified text only

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Schema

Column	Type	Description
text	string	The raw prompt text
label	int	0 = benign, 1 = malicious (prompt injection / jailbreak)

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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).

#	Source	Samples	Type
1	allenai/wildjailbreak	~262K	GPT-4 synthesized adversarial + vanilla prompts
2	yahma/alpaca-cleaned (SecAlign)	~104K	Clean instructions (benign) + synthetic injection wrappers (malicious)
3	TrustAIRLab/in-the-wild-jailbreak-prompts + verazuo/jailbreak_llms	~15K	Real-world jailbreak prompts + regular prompts
4	Chgdz/sentinel-jailbreak-detection	~12K	Unicode/encoding diverse threats (malicious subsampled to 3K)
5	xTRam1/safe-guard-prompt-injection	~8K	Diverse attack vectors + benign
6	neuralchemy/Prompt-injection-dataset	~6K	29 attack categories
7	WithSecure/injection-benchmark-rag	~2K	RAG-specific adversarial injections
8	jackhhao/jailbreak-classification	~1K	Roleplay jailbreaks + hard negatives
9	Lakera/gandalf_ignore_instructions	~1K	Real human CTF attacks
10	deepset/prompt-injections	~546	Political/social engineering injections
11	walledai/AdvBench	~520	Clean adversarial payloads
12	walledai/StrongREJECT	~313	Hard forbidden question set

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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.

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Trained Model

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

Metric	Score
AUC-ROC	0.9773
Accuracy	97.38%
F1	0.9758
Precision	98.00%
Recall	97.00%
ECE	0.053

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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.

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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/prompt-injection-judge-deberta-dataset}
}