Large Language Models in Cybersecurity

Andrei Kucharavy is the co-director of the Generative Learning Center at HES-SO Valais-Wallis. He holds a PhD from University of Paris-Sorbonne (2017), and is an engineer of Ecole Polytechnique (2013) and EPFL. Prior to this position he worked on counter-measures to the use of generative machine learning in offensive cyber-operations as a Distinguished Post-Doctoral Fellow at the Cyber-Defence Campus of armasuisse Science and Technology (S+T).Octave Plancherel is a study coordinator at the Cyber-Defence Campus of armasuisse S+T. He holds a Bachelor (2022) degree in Business Informatics from the University of Fribourg.Valentin Mulder is a Scientific Project Manager at the Cyber-Defence Campus of armasuisse S+T. He holds a Master (2022) degree in Legal Issues, Crime, and Security of Information Technologies from the University of Lausanne. Before his current position, he worked in the banking industry, particularly in the area of onlinefraud. In 2023, he co-edited the book “Trends in Data Protection and Encryption Technologies” published by Springer.Alain Mermoud is the Head of the Technology Monitoring team at the Cyber-Defence Campus of armasuisse S+T. He obtained his PhD (2019) in Information Systems from HEC Lausanne. His research interests lie at the intersection of information science, foresight, emerging technologies, and (cyber) threat intelligence. He co-edited and published over 30 peer-reviewed scientific articles in prestigious journals, such as Technological Forecasting and Social Change, Computers in Human Behavior, Knowledge-Based Systems, or Journal of Cybersecurity. In 2023, he co-edited the book “Trends in Data Protection and Encryption Technologies” published by Springer.Vincent Lenders is the founding Director of the Cyber-Defence Campus from armasuisse S+T. He holds a Master (2001) and PhD (2006) degree in electrical engineering and information technologies from ETH Zurich. He has contributed to developing and implementing various national cyber strategies at the Swiss Government and has published more than 150 technical papers on cyber security, data science and networking. In 2023, he co-edited the book “Trends in Data Protection and Encryption Technologies” published by Springer.

• Part I: Introduction.- 1. From Deep Neural Language Models to LLMs.- 2. Adapting LLMs to Downstream Applications.- 3. Overview of Existing LLM Families.- 4. Conversational Agents.- 5. Fundamental Limitations of Generative LLMs.- 6. Tasks for LLMs and their Evaluation.- Part II: LLMs in Cybersecurity.- 7. Private Information Leakage in LLMs.- 8. Phishing and Social Engineering in the Age of LLMs.- 9. Vulnerabilities Introduced by LLMs through Code Suggestions.- 10. LLM Controls Execution Flow Hijacking.- 11. LLM-Aided Social Media Influence Operations.- 12. Deep(er)Web Indexing with LLMs.- Part III: Tracking and Forecasting Exposure.- 13. LLM Adoption Trends and Associated Risks.- 14. The Flow of Investments in the LLM Space.- 15. Insurance Outlook for LLM-Induced Risk.- 16. Copyright-Related Risks in the Creation and Use of ML/AI Systems.- 17. Monitoring Emerging Trends in LLM Research.- Part IV: Mitigation.- 18. Enhancing Security Awareness and Education for LLMs.- 19. Towards Privacy Preserving LLMs Training.- 20. Adversarial Evasion on LLMs.- 21. Robust and Private Federated Learning on LLMs.- 22. LLM Detectors.- 23. On-Site Deployment of LLMs.- 24. LLMs Red Teaming.- 25. Standards for LLM Security.- Part V: Conclusion.- 26. Exploring the Dual Role of LLMs in Cybersecurity: Threats and Defenses.- 27. Towards Safe LLMs Integration.