Unmasking AI: A Comparative Analysis of Cyber Attack Vulnerabilities in Advanced Conversational Models

DOI

10.2991/978-94-6463-866-0_6How to use a DOI?

Keywords

LLM (Large Language Models); Cyberattacks; conversational AI security; ChatGPT; Gemini; Copilot; Meta AI

Abstract

As technological capabilities of conversational AI models grow so does their susceptibility to cyber threats which in turn raise critical security concerns. The potential of LLMs in aiding cyberattacks is diminutive but never zero. The study extends prior work by evaluating multiple LLMs on their effectiveness in assisting each attack type while broadening the scope of comparison using unique parameters to compare performance of each attack assisted by LLMs. This study focuses on a comparative analysis to assess capability of LLMs in generating malicious content which can be used to initiate malicious activities. The evaluation includes but is not limited to fac-tors such as attack performance, unique parameters for each attack and susceptibility to manipulation. LLM aided possible countermeasures for defensive strategy have been mentioned in this study. This study aims to minimize abuse of such vulnerabilities in conversational AI models which will help decrease malicious activity on the internet. project intends to improve AI security, encourage the safe deployment of large language models, and reduce their potential misuse in supporting cyberattacks. This dual nature of LLM needs to be rectified by their developers. This research raises awareness of the dual nature by educating legislators, security professionals, general public and developers on the evolving threat scenario. By discussing both sides, this study emphasizes the need for stronger defenses to prevent malicious exploitation while ensuring the ethical and secure application of conversational AI models.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vaishnavi Moorthy
AU  - Rupen Rupen
AU  - Dhruv Chopra
AU  - Anamika Jain
PY  - 2025
DA  - 2025/10/31
TI  - Unmasking AI: A Comparative Analysis of Cyber Attack Vulnerabilities in Advanced Conversational Models
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 46
EP  - 54
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_6
DO  - 10.2991/978-94-6463-866-0_6
ID  - Moorthy2025
ER  -