This page presents the Springer book “Large Language Models in Cybersecurity – Threats, Exposure and Mitigation” published in June, 2024. Podcasts summarizing the main findings of the book are available below the table.

Book ChapterUpdate Podcast
From Deep Neural Language Models to LLMsOverleafYouTube
Adapting LLMs to Downstream ApplicationsOverleafYouTube
Overview of Existing LLM FamiliesOverleafYouTube
Conversational AgentsOverleaf
Fundamental Limitations of Generative LLMsOverleafYouTube
Tasks for LLMs and Their EvaluationOverleafYouTube
LLMs in Cybersecurity
Private Information Leakage in LLMsOverleafYouTube
Phishing and Social Engineering in the Age of LLMsOverleafYouTube
Vulnerabilities Introduced by LLMs Through Code SuggestionsOverleafYouTube
LLM Controls Execution Flow HijackingOverleafYouTube
LLM-Aided Social Media Influence OperationsOverleaf
Deep(er) Web Indexing with LLMsOverleafYouTube
Tracking and Forecasting Exposure
LLM Adoption Trends and Associated RisksOverleaf
The Flow of Investments in the LLM SpaceOverleafYouTube
Insurance Outlook for LLM-Induced RiskOverleafYouTube
Copyright-Related Risks in the Creation and Use of ML/AI SystemsOverleaf
Monitoring Emerging Trends in LLM ResearchOverleaf
Enhancing Security Awareness and Education for LLMsOverleafYouTube
Towards Privacy Preserving LLMs TrainingOverleafYouTube
Adversarial Evasion on LLMsOverleaf
Robust and Private Federated Learning on LLMsOverleaf
LLM DetectorsOverleafYouTube
On-Site Deployment of LLMsOverleaf
LLMs Red TeamingOverleaf
Standards for LLM SecurityOverleaf
Exploring the Dual Role of LLMs in Cybersecurity: Threats and DefensesOverleaf
Towards Safe LLMs IntegrationOverleafYouTube
This table reflects the table of content of the book. Each chapter is kept up to date by its authors in Overleaf.

All podcasts recorded at the 2024 LLMs in Cybersecurity Track at the Cyber Alp Retreat

Full Book

Source: Springer