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Security Risks in Large Language Models and General Mitigation Strategies

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Ruidong Zhang
Modimowabarwa Kanyane
Published in
Advancing Innovative Cybersecurity Solutions and Approaches to Protect Digital Ecosystems
Publisher
Springer Nature Switzerland

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Abstract

This study delves into the transformative potential of Large Language Models (LLMs) like ChatGPT and DeepSeek, highlighting their impact on industries such as education, healthcare, and investment services. It systematically identifies and categorizes six primary risks associated with LLMs: ethical risks, data privacy and security, misinformation, bias and fairness, academic integrity, and environmental impacts. Each risk category is explored in detail, including root causes and specific examples. The study also proposes a range of mitigation strategies, from regulatory frameworks and encryption technologies to fact-checking mechanisms and bias detection algorithms. Additionally, it discusses the environmental impacts of LLMs, including their carbon footprint and resource consumption, and suggests strategies for mitigation. The study concludes by highlighting areas for future research, such as the development of standardized evaluation frameworks and scalable mitigation tools.
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next chapter AI-Blockchain Synergy for Next-Generation Cybersecurity. Adaptive, Ethical, and Efficient Architectures
Title
Security Risks in Large Language Models and General Mitigation Strategies
Authors
Ruidong Zhang
Modimowabarwa Kanyane
Copyright Year
2026
Book
Advancing Innovative Cybersecurity Solutions and Approaches to Protect Digital Ecosystems

Print ISBN: 978-3-032-13074-7

Electronic ISBN: 978-3-032-13075-4

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-13075-4_1
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