As AI capabilities expand and integrate deeper into sensitive systems, the potential for misuse, exploitation, and manipulation grows significantly. This book serves as a comprehensive technical guide to securing Artificial Intelligence against these evolving threats. It moves beyond theoretical policy to provide a "defense in depth" strategy, emphasizing that a single layer of protection is insufficient.

The content is structured around three core pillars of AI defense:

LLM Guardrails: Application-layer safeguards that validate inputs and outputs to neutralize vectors like prompt injection and data leakage before they reach the model or user.

Adversarial Training: A method to strengthen neural networks during development by exposing them to mathematical perturbations (like FGSM attacks) within the training loop, forcing the model to stabilize its decision boundaries.

Adversarial Tuning: A behavioral alignment technique for Large Language Models (LLMs) that uses Supervised Fine-Tuning (SFT) and Low-Rank Adaptation (LoRA) to teach models to intrinsically recognize and refuse complex semantic attacks, such as jailbreaking and priming.

Who Is This Book For?

This book is designed for technical practitioners who need to move from filtering inputs to hardening the model itself. It is specifically tailored for:

AI Engineers and Developers: Who face the engineering challenge of implementing safeguards without destroying the model's utility or introducing excessive latency. It covers practical implementations using Python libraries like Pydantic, Guardrails AI, and Unsloth.

Security Professionals and Red Teamers: Who need to understand the mechanics of evasion, such as how "priming" attacks exploit autoregressive generation or how gradient-based attacks weaponize the loss function.

Machine Learning Researchers: Interested in the robustness-accuracy frontier and how techniques like Epsilon Spread Training can prevent models from overfitting to specific noise magnitudes.

Table of Contents

• Intro
• LLM Guardrails
• Adversarial Training
• The Training Pipeline
• Baseline vs. Defense
• LLM Adversarial Tuning
• From Jailbreaks to Priming
• Token-Level Manipulation

Page Count: 162

Format: PDF

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