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    Large Language Models in Cybersecurity

    Threats, Exposure and Mitigation

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    Contributor(s)
    Kucharavy, Andrei (editor)
    Plancherel, Octave (editor)
    Mulder, Valentin (editor)
    Mermoud, Alain (editor)
    Lenders, Vincent (editor)
    Language
    English
    Show full item record
    Abstract
    This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the increased availability of powerful large language models (LLMs) and how they can be mitigated. It attempts to outrun the malicious attackers by anticipating what they could do. It also alerts LLM developers to understand their work's risks for cybersecurity and provides them with tools to mitigate those risks. The book starts in Part I with a general introduction to LLMs and their main application areas. Part II collects a description of the most salient threats LLMs represent in cybersecurity, be they as tools for cybercriminals or as novel attack surfaces if integrated into existing software. Part III focuses on attempting to forecast the exposure and the development of technologies and science underpinning LLMs, as well as macro levers available to regulators to further cybersecurity in the age of LLMs. Eventually, in Part IV, mitigation techniques that should allow safe and secure development and deployment of LLMs are presented. The book concludes with two final chapters in Part V, one speculating what a secure design and integration of LLMs from first principles would look like and the other presenting a summary of the duality of LLMs in cyber-security. This book represents the second in a series published by the Technology Monitoring (TM) team of the Cyber-Defence Campus. The first book entitled "Trends in Data Protection and Encryption Technologies" appeared in 2023. This book series provides technology and trend anticipation for government, industry, and academic decision-makers as well as technical experts.
    URI
    https://library.oapen.org/handle/20.500.12657/90897
    Keywords
    large language models; cybersecurity; cyberdefense; neural networks; societal implications; risk management; LLMs
    DOI
    10.1007/978-3-031-54827-7
    ISBN
    9783031548277, 9783031548260, 9783031548277
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2024
    Imprint
    Springer Nature Switzerland
    Classification
    Artificial intelligence
    Computer security
    Network security
    Pages
    247
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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