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cyberec/llm-prompt-injection-attacks

published by @cyberec

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
619
Last 30 days
77
Likes
2
Size
12.8 MB
Created on HF
2026-04-12
Age
6 months ago

Description

Prompt Injection Mechanisms Dataset

Overview

A 55,000-sample multi-label dataset for prompt injection detection in large language models.

Labels

- BENIGN

- JAILBREAK

- INSTRUCTION_OVERRIDE

- ROLE_HIJACK

- DATA_EXFILTRATION

Format

The dataset is provided in Apache Parquet format with train/validation splits.

Construction

The dataset was created by merging multiple public prompt-injection datasets and

re-annotating… See the full description on the dataset page: https://huggingface.co/datasets/cyberec/llm-prompt-injection-attacks.

Tags

task_categories:text-classificationlanguage:enlicense:apache-2.0size_categories:10K<n<100Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:usprompt-injectionllm-securityjailbreakai-safety

README current version from Hugging Face

language: en
license: apache-2.0
task_categories:

  • text-classification
  • multi-label-classification
    tags:
  • prompt-injection
  • llm-security
  • jailbreak
  • ai-safety

Prompt Injection Mechanisms Dataset

Overview

A 55,000-sample multi-label dataset for prompt injection detection in large language models.

Labels

- BENIGN
- JAILBREAK
- INSTRUCTION_OVERRIDE
- ROLE_HIJACK
- DATA_EXFILTRATION

Format

The dataset is provided in Apache Parquet format with train/validation splits.

Construction

The dataset was created by merging multiple public prompt-injection datasets and
re-annotating them using a deterministic rule-based taxonomy to identify attack mechanisms.

Sources

- qualifire/prompt-injections-benchmark
- jayavibhav/prompt-injection-safety

Original datasets are not redistributed.

Intended Use

Research on LLM security, prompt injection detection, and AI safety.

Limitations

Labels are heuristic-based and may contain noise.

README history 1 revision

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-04-12Duplicate from Smooth-3/llm-prompt-injection-attacksa3d05891 KB
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

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Metadata

License
apache-2.0

Repository

FILE .gitattributes FILE README.md FILE train.parquet FILE validation.parquet

File contents live on Hugging Face. Click a file or folder to open it there.

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("cyberec/llm-prompt-injection-attacks")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="cyberec/llm-prompt-injection-attacks", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/cyberec/llm-prompt-injection-attacks
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