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
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
收藏
页码:404 / 416
相关论文
共 50 条
  • [31] Prompt injection attacks on vision language models in oncology
    Jan Clusmann
    Dyke Ferber
    Isabella C. Wiest
    Carolin V. Schneider
    Titus J. Brinker
    Sebastian Foersch
    Daniel Truhn
    Jakob Nikolas Kather
    Nature Communications, 16 (1)
  • [32] Unleashing ChatGPT's Power: A Case Study on Optimizing Information Retrieval in Flipped Classrooms via Prompt Engineering
    Wang, Mo
    Wang, Minjuan
    Xu, Xin
    Yang, Lanqing
    Cai, Dunbo
    Yin, Minghao
    IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, 2024, 17 : 629 - 641
  • [33] Factors that Motivate Defense Against Social Engineering Attacks Across Organizations
    Tawalbeh, Lo'ai A.
    Muheidat, Fadi
    18TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS, FNC 2023/20TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING, MOBISPC 2023/13TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY, SEIT 2023, 2023, 224 : 75 - 82
  • [34] Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model
    Shin, Euibeom
    Ramanathan, Murali
    JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2024, 51 (02) : 101 - 108
  • [35] Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model
    Euibeom Shin
    Murali Ramanathan
    Journal of Pharmacokinetics and Pharmacodynamics, 2024, 51 : 101 - 108
  • [36] MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering
    Zhu, Keyun
    Huang, Mengting
    Wang, Yimeng
    Gu, Yaxin
    Li, Weihua
    Liu, Guixia
    Tang, Yun
    BRIEFINGS IN BIOINFORMATICS, 2024, 25 (05)
  • [37] Data Stealing Attacks against Large Language Models via Backdooring
    He, Jiaming
    Hou, Guanyu
    Jia, Xinyue
    Chen, Yangyang
    Liao, Wenqi
    Zhou, Yinhang
    Zhou, Rang
    ELECTRONICS, 2024, 13 (14)
  • [38] Large Language Models for Software Engineering: A Systematic Literature Review
    Hou, Xinyi
    Zhao, Yanjie
    Liu, Yue
    Yang, Zhou
    Wang, Kailong
    Li, Li
    Luo, Xiapu
    Lo, David
    Grundy, John
    Wang, Haoyu
    ACM Transactions on Software Engineering and Methodology, 2024, 33 (08)
  • [39] JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models
    Feng, Yingchaojie
    Chen, Zhizhang
    Kang, Zhining
    Wang, Sijia
    Zhu, Minfeng
    Zhang, Wei
    Chen, Wei
    arXiv,
  • [40] Requirements Engineering and Large Language Models: Insights From a Panel
    Borg, Markus
    IEEE SOFTWARE, 2024, 41 (02) : 6 - 10