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Large Language Models and Cybersecurity: A Mathematical Preface on Adversarial Risk

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Description

This book reframes cybersecurity in the age of large language models and emerging AGI as a dynamic systems problem, rather than a collection of isolated vulnerabilities. It shows how probabilistic generative models introduce fundamentally new attack surfaces such as prompt injection, data leakage, and adversarial manipulation, while also reshaping defense through automation, reasoning augmentation, and adaptive detection. Moving beyond model-level concerns, the book traces how these risks propagate across enterprise architectures, critical infrastructure, and global systems, emphasizing that security failures are rarely local. Instead, they emerge from coupling across layers where small weaknesses can cascade into large-scale disruptions. Building on this perspective, the book develops a unifying principle: stability depends on whether forces that amplify risk, such as automation, economic scaling, and adversarial adaptation, are outweighed by forces that dampen it, such as governance, defensive engineering, and coordination. Through this lens, AI-driven conflict, multi-agent competition, and even AGI-level dynamics are analyzed as systems that can become unstable, irreversible, or globally coupled. The central conclusion is both cautionary and practical: catastrophic risk is not inevitable, but it is structurally possible. Stability must therefore be engineered deliberately across technical, organizational, and geopolitical layers, making cybersecurity in the AI era as much a problem of system design and control as it is of detection and defense.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
18 April 2026
Listed Since
19 April 2026

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