Mitigating the Misuse of Generative AI: Navigating the Emerging Threat Landscape and Modern Security Paradigms
  IJCTT-book-cover
 		          
 
© 2024 by IJCTT Journal
Volume-72 Issue-9
Year of Publication : 2024
Authors : Sharat Ganesh
DOI :  10.14445/22312803/IJCTT-V72I9P103

How to Cite?

Sharat Ganesh, Samara Antonia Burris, "Mitigating the Misuse of Generative AI: Navigating the Emerging Threat Landscape and Modern Security Paradigms," International Journal of Computer Trends and Technology, vol. 72, no. 9, pp. 14-17, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I8P103

Abstract
Generative AI has emerged as a transformative technology with wide-ranging applications across industries. However, its capabilities also introduce significant security risks that must be carefully managed. This paper examines the key threats facing generative AI systems, including data poisoning, model stealing, and adversarial attacks. It outlines a modern security paradigm to mitigate these risks, encompassing data quality and validation, model protection, adversarial robustness, and continuous monitoring. Through an analysis of recent case studies and emerging research, the paper argues that a comprehensive, multi-layered approach to security is essential for realizing the benefits of generative AI while minimizing potential negative impacts. The consequences of security breaches, including reputational damage, financial losses, and potential national security implications, are discussed. The findings highlight the need for ongoing vigilance and collaboration across the AI community to address the evolving threat landscape. This research contributes to the growing body of knowledge on AI security and provides practical insights for developers, users, and policymakers involved in the deployment of generative AI technologies.

Keywords
Data Poisoning, Generative AI, Model Protection, Cybersecurity, Security Risks.

Reference

[1] Tom B. Brown et al., “Language Models are Few-Shot Learners,” arXiv Preprint, pp. 1-75, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Xinyun Chen et al., “Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning,” arXiv Preprint, pp. 1-18, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] J.X. Dempsey, “Generative AI: The Security and Privacy Risks of Large Language Models,” NetChoice, pp. 1-24, 2023.
[Google Scholar] [Publisher Link]
[4] Anas Baig, Generative AI Security: 8 Risks That You Should Know, GlobalSign, 2023. [Online]. Available: https://www.globalsign.com/en/blog/8-generative-ai-security-risks
[5] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy, “Explaining and Harnessing Adversarial Examples,” arXiv Preprint, pp. 1- 11, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Amirreza Shaeiri, Rozhin Nobahari, and Mohammad Hossein Rohban, “Towards Deep Learning Models Resistant to Large Perturbations,” arXiv Preprint, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] James Jordon, Jinsung Yoon, and Mihaela van der Schaar, “Measuring the Quality of Synthetic Data for Use in Competitions,” arXiv Preprint, pp. 1-3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mika Juuti et al., “PRADA: Protecting Against DNN Model Stealing Attacks,” Proceedings of the 2019 IEEE European Symposium on Security and Privacy, Stockholm, Sweden, pp. 512-527, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Nicholas Carlini et al., “Extracting Training Data from Large Language Models,” Proceedings of the 31st USENIX Security Symposium, pp. 2633-2650, 2022.
[Google Scholar] [Publisher Link]
[10] Ponemon Institute, Cost of a Data Breach Report 2022, IBM Security, 2022.
[11] Florian Tramèr et al., “Stealing Machine Learning Models via Prediction APIs,” 25th USENIX Security Symposium, pp. 601-618, 2016.
[Google Scholar] [Publisher Link]
[12] Bolun Wang et al., “Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks,” 2019 IEEE Symposium on Security and Privacy, San Francisco, CA, USA, pp. 707-723, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Chaowei Xiao et al., “Generating Adversarial Examples with Adversarial Networks,” Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3905-3911, 2022.
[CrossRef] [Google Scholar] [Publisher Link]