datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SHIELDINGThis dataset was generated as a part of our paper "Towards Secure Prompt Processing: A Unified Framework to Detect, Sanitize, and Prevent Adversarial Prompts via Dual-Optimized Threshold-Aware Learning".
SHIELDING dataset was generated by combining and filtering the following datasets:
Prompt Injection Hackaprompt GPT35, https://huggingface.co/datasets/imoxto/prompt_injection_hackaprompt_gpt35.
JailBreak V-28k, https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k.
Open Platypus… See the full description on the dataset page: https://huggingface.co/datasets/ata8e/SHIELDING.Rash-Driving-Detection-on-Bikes-for-ML
Dataset Card for Rash Driving Detection on Bikes Using Mobile and Sensor Data
This dataset is designed to aid the detection of rash driving behavior on bikes using data collected from mobile and sensor-based systems. It includes sensor readings such as accelerometer values, orientation (azimuth, pitch, roll), and speed, with labels indicating whether the riding behavior is classified as rash or not.
Dataset Details
Dataset Description
This dataset consists of… See the full description on the dataset page: https://huggingface.co/datasets/ShieldX/Rash-Driving-Detection-on-Bikes-for-ML.Context-Aware-Repository-Prompt-Injection
Overview
This dataset is designed for training and evaluating AI security scanners that detect repository-aware prompt injection attacks in software development and code-assistant environments.
Repository-aware prompt injections are malicious instructions embedded in code repositories, documentation, comments, configuration files, issue trackers, or other project artifacts that attempt to manipulate an AI system's behavior, override its instructions, exfiltrate sensitive… See the full description on the dataset page: https://huggingface.co/datasets/ShieldX/Context-Aware-Repository-Prompt-Injection.SHIELDING_LLaMA-3_ResponseThis dataset is a part of our SHIELDING dataset that progressed to LLaMA-3 and checked each response humanly, as a part of our paper "Towards Secure Prompt Processing: A Unified Framework to Detect, Sanitize, and Prevent Adversarial Prompts via Dual-Optimized Threshold-Aware Learning".