AI Security Engineering: Red Team & Blue Team Study Notes
This book is designed for AI engineers, cyber security professionals, SOC analysts, AI Red Teamers, penetration testers, machine learning practitioners, malware researchers, and security architects seeking a modern understanding of AI Security in real-world environments.
Across its chapters, readers will learn how modern AI systems fail under adversarial pressure, why traditional accuracy metrics create dangerous illusions of safety, and how attackers weaponize gradients, prompt structures, semantic manipulation, and token-level behavior to compromise models.
The book systematically explores AI guardrails, adversarial machine learning, LLM defense strategies, prompt injection mitigation, semantic validation pipelines, AI-based content moderation, and defense-in-depth architectures specifically designed for modern generative AI ecosystems.
The material also dives deeply into AI Red Teaming methodologies, demonstrating how offensive techniques expose weaknesses in model alignment, inference pipelines, and deployment architecture.
Readers will examine real adversarial workflows including jailbreak engineering, token manipulation, malicious prompt chaining, inference abuse, and gradient-based attacks such as FGSM and I-FGSM.
Unlike beginner-level AI books that remain theoretical, this handbook emphasizes implementation. Readers will build practical guardrails using Python, Pydantic, Guardrails AI, semantic validators, and enterprise-grade filtering pipelines.
The book also explores external AI defense services, scalable AI security architectures, detection engineering strategies, and methods for securing AI-enabled applications operating in production environments.
Table of Contents
- Intro
- LLM Guardrails
- Character-Based Validation
- Traditional Content-based Validation
- AI-Based Guardrails
- Guardrail Libraries
- Guardrails as a Service
- Adversarial Training
- The Vanity of Accuracy
- Why Standard Training is a Trap
- The Only Viable Defense: Adversarial Training
- The Mechanics of Evasion
- The Anti-Training Loop
- Epsilon
- The Geometry of Failure
- The Curse of Dimensionality
- I-FGSM
- Overfitting
- Implementation and Mechanics
- Dynamic Augmentation
- The Integrated Training Loop
- The Mathematical Foundation
- The Robustness-Accuracy Frontier
- Epsilon Spread Training
- Implementation Logic
- Performance Comparison
- Hyperparameters
- Computational Cost
- Limitations
- Component Construction
- Environment Provisioning
- Defining the Operational Constraints
- Serializing the Attack Vectors
- Implementation
- Technical Analysis
- The Architecture: LeNet-5
- FGSM Implementation
- The Evaluation Benchmark: I-FGSM
- Adversarial Evaluation
- The Baseline
- Generating the Benchmark
- The Generator Function
- Implementing the Training Loop
- Execution: The Training Pipeline
- Evaluation and Metrics
- Parsing the Telemetry
- The Degradation Curve
- Baseline vs. Defense
- Troubleshooting
- LLM Adversarial Tuning
- The Philosophy of Defense in Depth
- Why RLHF Isn't Enough
- Adversarial Tuning
- Threat Model Analysis
- From Jailbreaks to Priming
- Token-Level Manipulation
- Defensive Strategy
- Limitations & The Road Ahead
- Supervised Fine-Tuning
- Low-Rank Adaptation (LoRA)
- The Architecture of the Dataset
- The Generalization Challenge
- Mitigating Over-Refusal
- Environment Setup & Data Preparation
- Exploratory Data Analysis (EDA)
- Data Composition Strategy
- Data Serialization
- Assembling the Training Corpus
- Pipeline Completion
- Architecting the Training Environment
- Operational Verification
- Evaluation and Validation
- Comparative Analysis
- Metric Decomposition
- Optimization & Remediation Strategies
- Iterative Development
Page Count: 110
Format: PDF
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