Prompt Engineering: Unleashing the Power of Large Language Models to Defend Against Social Engineering Attacks

被引：0

作者：

Nezer, Ahmed I. ^{[1
]}

Nema, Bashar M. ^{[1
]}

Salim, Wisam Makki ^{[2
]}

机构：

[1] Department of Computer, College of Science, Mustansiriyah University, Baghdad,14022, Iraq

[2] College of Dentistry, Al-Iraqia University, Baghdad,10011, Iraq

来源：

Iraqi Journal for Computer Science and Mathematics | 2024年 / 5卷 / 03期

关键词：

Computer simulation languages;

D O I：

10.52866/ijcsm.2024.05.03.024

中图分类号：

学科分类号：

摘要：

Prompt Engineering is an emerging area of study that pertains to the act of conceptualizing, perfecting, and executing prompts that guide an AI model to an intended purpose. The AI model is an LLM, which they are the hit of our time and probably the controversial type of AI. They are capable of executing several tasks using natural language processing algorithms. Due to their ease of use and fast development, they are becoming highly dependent. We found that to interact correctly with these models and gain the best performance, several techniques should be taken into consideration. Moreover, there are additional methods or tips to write a good prompt. © 2024 College of Education, Al-Iraqia University. All rights reserved.

引用

下载

页码：404 / 416

共 50 条

[41] Large Language Models for Software Engineering: Survey and Open Problems
Fan, Angela
Gokkaya, Beliz
Harman, Mark
Lyubarskiy, Mitya
Sengupta, Shubho
Yoo, Shin
Zhang, Jie M.
2023 IEEE/ACM INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: FUTURE OF SOFTWARE ENGINEERING, ICSE-FOSE, 2023, : 31 - 53
[42] The Return of Formal Requirements Engineering in the Era of Large Language Models
Spoletini, Paola
Ferrari, Alessio
REQUIREMENTS ENGINEERING: FOUNDATION FOR SOFTWARE QUALITY, REFSQ 2024, 2024, 14588 : 344 - 353
[43] Towards an understanding of large language models in software engineering tasks
Zibin Zheng
Kaiwen Ning
Qingyuan Zhong
Jiachi Chen
Wenqing Chen
Lianghong Guo
Weicheng Wang
Yanlin Wang
Empirical Software Engineering, 2025, 30 (2)
[44] Challenges in applying large language models to requirements engineering tasks
Norheim, Johannes J.
Rebentisch, Eric
Xiao, Dekai
Draeger, Lorenz
Kerbrat, Alain
de Weck, Olivier L.
DESIGN SCIENCE, 2024, 10
[45] Why Large Language Models will (not) Kill Software Engineering Research
Di Penta, Massimiliano
PROCEEDINGS OF 2024 28TH INTERNATION CONFERENCE ON EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING, EASE 2024, 2024, : 5 - 5
[46] Large Language Models in Software Engineering Education: A Preliminary Study on Software Requirements Engineering Courses
Feng Chen
Shaomin Zhu
Xin Liu
Ying Qian
计算机教育, 2025, (03) : 24 - 33
[47] Strategies for counteracting social engineering attacks
Fatima R.
Yasin A.
Liu L.
Jianmin W.
Computer Fraud and Security, 2022, 2022 (07):
[48] A framework for conceptualizing social engineering attacks
Gonzalez, Jose J.
Sarriegi, Jose M.
Gurrutxaga, Alazne
CRITICAL INFORMATION INFRASTRUCTURES SECURITY, 2006, 4347 : 79 - +
[49] Flexible and Secure Code Deployment in Federated Learning using Large Language Models: Prompt Engineering to Enhance Malicious Code Detection
Seo, Jungwon
Zhang, Nan
Rong, Chunming
2023 IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE, CLOUDCOM 2023, 2023, : 341 - 349
[50] Optimierte Interaktion mit Large Language ModelsPraxisorientierter Leitfaden zu Prompt Engineering und Retrieval-Augmented GenerationOptimized interaction with Large Language ModelsA practical guide to Prompt Engineering and Retrieval-Augmented Generation
Anna Fink
Alexander Rau
Elmar Kotter
Fabian Bamberg
Maximilian Frederik Russe
Die Radiologie, 2025, 65 (4) : 235 - 242

← 1 2 3 4 5 →