A Prompt Injection Attack Dataset and Prompt Collection for Evaluating Defense-in-Depth Security in LLM-Based Systems

This repository provides the dataset and prompt collection used in the study titled “A Defense-in-Depth Approach to Mitigating Prompt Injection in LLM-Based Systems.” The materials are designed to support reproducibility and transparency of the experimental evaluation conducted in the study.

The repository consists of two main components:

1. Prompt Injection Attack Dataset

The dataset is provided in the file dataset.docx and contains a curated set of prompt injection attack instances. The dataset is systematically structured and categorized to represent commonly observed attack types in LLM-based systems.

The dataset includes the following five categories:

Instruction OverridePrompts attempting to override or ignore system-level instructions.

Role ManipulationPrompts designed to force the model into unauthorized or privileged roles.

System Prompt LeakagePrompts aiming to extract hidden system or developer-defined instructions.

Data Exfiltration AttemptsPrompts targeting the disclosure of sensitive or restricted information.

Indirect InjectionContext-based attacks where malicious instructions are embedded within external or retrieved content.

Each category contains a balanced number of samples to ensure fair and controlled experimental comparisons across attack types.

2. Prompt Collection for System Design

The file prompt_article.docx contains all prompts used in the system design and experimental evaluation.

This includes:

Input prompts used in attack scenarios

System prompts defining model behavior

Policy enforcement prompts

Retrieval-related prompts (for RAG scenarios)

Output validation and guardrail prompts

These prompts collectively represent the operational configuration of the evaluated LLM-based system and enable reproducibility of the experimental setup.

Purpose and Usage

The dataset and prompt collection are intended for:

Evaluating prompt injection attacks in LLM-based systems

Benchmarking security mechanisms such as defense-in-depth architectures

Supporting research on LLM security, prompt engineering, and adversarial robustness

Reproducing the experimental results reported in the associated study

Researchers can use this dataset to analyze attack behavior, test defense strategies, or extend the experimental framework to different models and architectures.

Reproducibility

All experiments in the associated study were conducted using the provided prompts and dataset under controlled conditions. The materials included in this repository allow other researchers to replicate the experimental scenarios and validate the reported findings.

Notes

The dataset is synthetically constructed based on patterns and attack strategies reported in the literature.

The prompts are designed for research purposes and do not reflect real user data.

No sensitive or personal data is included in this repository.