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SecQA is a specialized dataset created for the evaluation of Large Language Models (LLMs) in the domain of computer security. It consists of multiple-choice questions aimed at assessing the understanding and application of LLMs' knowledge in computer security.
SecQA is an innovative dataset designed to benchmark the performance of Large Language Models (LLMs) in the field of computer security. It contains a series of multiple-choice questions generated by GPT-4, based on the content from the textbook Computer Systems Security: Planning for Success.
The dataset is structured into two versions:
This design allows for a preliminary evaluation of LLMs across different levels of complexity in understanding and applying computer security principles.
Each question in the dataset offers four answer choices, with only one being the correct answer. To ensure fairness and eliminate any bias in question design, the answer choices have been carefully shuffled.
Question: What is the purpose of implementing a Guest Wireless Network in a corporate environment?
Options:
Answer: D
Explanation: A Guest Wireless Network provides visitors with internet access while segregating them from the main corporate network, enhancing security by preventing unauthorized access to sensitive company resources.
Question: What is a typical indicator that an Intrusion Detection System (IDS) or Intrusion Prevention System (IPS) might identify as a network attack?
Options:
Answer: A
Explanation: IDS/IPS systems monitor network traffic and can identify network attacks by detecting anomalies, strange behaviors, or known exploit signatures in the traffic.
The primary application of SecQA is to serve as a benchmark for testing and evaluating the capabilities of LLMs in the domain of computer security.
The SecQA dataset is primarily intended for evaluating and benchmarking the performance of Large Language Models (LLMs) in understanding and applying principles of computer security. It's suitable for:
SecQA is not designed for and should not be used as:
@article{liu2023secqa,
title={SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security},
author={Liu, Zefang},
journal={arXiv preprint arXiv:2312.15838},
year={2023}
}
Information
Organization
BenchFlow
Release Date
April 18, 2025