The Applied AI Universe Coding Guide: Adversarial Attacks
A Hands-On Handbook for Attacking and Measuring Every AI Model
About This Book
Every number in this book came from a real, executed notebook cell. Nothing was fabricated, and nothing was re-run to produce a more convenient result. The Applied AI Universe Coding Guide: Adversarial Attacks takes every model Book 1 built, from a rule-based expert system through deep neural networks, transformers, RLHF-aligned language models, and hybrid quantum-classical circuits, and puts each one under a real, executed attack.
Each chapter follows the same repeatable structure: threat model, victim, baseline, attack, real output, benchmark, and mitigation preview. You'll poison classifiers, hijack attention mechanisms, extract training data, backdoor a LoRA adapter, and destabilize a variational quantum circuit — all with real code and a shared scorecard you can compare chapter to chapter. When an attack barely worked, or failed outright, that gap is stated plainly rather than papered over.
A dedicated benchmarking chapter consolidates every result into one master table and holds this book's own methodology up against the field's real gold standards, AutoAttack and HarmBench. A final chapter turns a raw scorecard into a professional red-team report, the bridge into Book 3's defenses.
No hand-waving, no invented statistics. Just real attacks, real failures, and real numbers you can check yourself.
What's Inside
37 chapters across 9 parts. Three foundational chapters build the threat-modeling framework and shared measurement harness; thirty-two hands-on chapters attack a real Book 1 model each; two closing chapters consolidate every result and turn it into a professional report.
- Part I — Foundations of Adversarial AI — Ch. 1–3: Threat Models, Taxonomy & Perturbation Budgets, the Measurement Harness & Victim Gallery.
- Part II — Attacking Symbolic AI — Ch. 4–6: Planning & Scheduling, Expert Systems, Fuzzy Logic.
- Part III — Attacking Machine Learning — Ch. 7–11: Feature-Space Poisoning, Supervised, Unsupervised, Semi-Supervised, and Ensemble Learning.
- Part IV — Attacking Neural Networks — Ch. 12–16: Activations, the Perceptron, Backpropagation, CNNs & Sequence Models, Self-Organizing Maps.
- Part V — Attacking Deep Learning — Ch. 17–23: AutoAttack on Deep Networks, Transfer Learning & Backdoors, GANs, Attention, Dropout, Deep Reinforcement Learning, Capsule Networks & DBNs.
- Part VI — Attacking Generative AI — Ch. 24–31: Language Models, the Transformer, Pre-Trained NLU, Dialogue & Jailbreaking, Diffusion Models, LoRA/PEFT, RLHF, State-Space Models.
- Part VII — Attacking Hybrid Quantum-Classical AI — Ch. 32: gradient attacks on a variational quantum classifier.
- Part VIII — Attacking Quantum AI — Ch. 33–35: Quantum Adversarial Examples, QAOA parameter poisoning, VQE.
- Part IX — Benchmarking & Red-Team Reporting — Ch. 36–37: the Cross-Model Scorecard measured against AutoAttack and HarmBench, and writing a professional red-team report.
Who This Book Is For
Readers of Book 1, or anyone comfortable with Python who wants to understand exactly how AI models break, not just that they can. If you build AI systems and need to know what an attacker actually sees, start here.
The Adaptive AI Codex Series
This title is Book 2 of The Adaptive AI Codex Series — a practical, code-first collection for anyone interested in building, understanding, and securing AI systems. Series status: Books 1 & 2 Available · Book 3 Coming in 2026. See the full series overview.