Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense

TL;DR

This paper reviews the development, capabilities, limitations, and future prospects of generative large language models, emphasizing their implications for cyber-defense and operational environments, especially in Switzerland.

Contribution

It provides a comprehensive overview of generative language models, highlighting their evolution, real-world applications, and potential security concerns in cyber-defense contexts.

Findings

Generative models have advanced significantly in recent years.

Their real-world deployment reveals both potentials and risks.

Cyber-defense applications are a key future focus.

Abstract

Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point of public attention has been such a refinement of the GPT3 model -- the ChatGPT and its subsequent integration with auxiliary capabilities, including search as part of Microsoft Bing. Despite extensive prior research invested in their development, their performance and applicability to a range of daily tasks remained unclear and niche. However, their wider utilization without a requirement for technical expertise, made in large part possible through conversational fine-tuning, revealed the extent of their true capabilities in a real-world environment. This has garnered both public excitement for their potential applications and concerns about their capabilities and potential malicious uses. This review aims to provide a brief overview of the history, state of the art, and implications of Generative Language Models in terms of their principles, abilities, limitations, and future prospects -- especially in the context of cyber-defense, with a focus on the Swiss operational environment.