The DOI matches its Crossref record.
Cyber Security Issues and Challenges Related to Generative AI and ChatGPT
Abstract
In recent years, Generative Artificial Intelligence (AI) and ChatGPT (Generative Pre-trained Transformer) models that are capable of generating realistic human-mimicked languages have gained progressive popularity. With the evolution of technology, there has been a significant increase in the availability and usage of artificial intelligence tools, such as ChatGPT and Generative AI, that will assist in shaping the future. However, this increasing popularity poses a potential risk if used inappropriately. Threats from AI pose special challenges for government, the private sector, and national security. In this paper, we address some of key concerns of significant cyber security issues and challenges related to Generative AI and ChatGPT. With careful consideration to application usage, organizations can implement appropriate security measures to mitigate these risks. We also incorporate recommendations about ChatGPT usage and its impact on society. It is important that researchers, developers, and policymakers (CIOs, CSOs) work together to mitigate these risks and to ensure that these models are used in a responsible and ethical manner.
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These vulnerabilities highlight the double-edged nature of LLM applications in cybersecurity.
“These vulnerabilities highlight the double-edged nature of LLM applications in cybersecurity (Pasupuleti et al. 2023).”
From When LLMs meet cybersecurity: a systematic literature review (2025) · cites this paper as Pasupuleti et al. 2023
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No references could be resolved yet.
Research works printed but not linked: 15
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- 10.32388/QGMSQG · DOI
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Cited by
Sorted by citations
When LLMs meet cybersecurity: a systematic literature review
Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 383
“Researchers are motivated to find new ways to automatically identify and correct bugs/vulnerabilities with LLMs (Zhang et al. 2024d), as shown in Table 10.”
Metadata
- Authors
- Pasupuleti, R., Vadapalli, R., & Mader, C.
- Affiliations
- University of Miami (US)
- Venue
- 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS)
- Publisher
- IEEE
- Published
- 2023-11-21
- Pages
- 1-5
- OpenAlex
- W4390493700
- Cited by
- 26
- References
- 0
- Citations by year
- 2026: 2 · 2025: 16 · 2024: 8
- FWCI
- 4.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Social Networks Analysis, Management and Security (SNAMS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- generative AI, ChatGPT, cybersecurity risks, AI risks, national security, AI ethics, responsible AI use, security measures, societal impact of AI
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