> For the complete documentation index, see [llms.txt](https://arif-playbook.gitbook.io/100-days-of-ai-sec/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://arif-playbook.gitbook.io/100-days-of-ai-sec/readme.md).

# Introduction

Welcome to my learning journey on AI/ML Security.\
This series contains daily notes, LinkedIn posts, and learnings.

👉 Start reading from [Day 1 AL vs ML vs DL](broken://pages/x17bfc54XQLohRDeZVWQ)

## 🧠 Phase 1: Build a Solid Foundation (Days 1–20)

**Goal:** Understand the basics of AI/ML so you can later secure it

### Topics

* What is AI, ML, DL? Differences?
* Supervised, Unsupervised, Reinforcement Learning
* Common ML algorithms (Linear Regression, Decision Trees, SVM, etc.)
* Overfitting, Underfitting, Bias-Variance Tradeoff
* Feature engineering and model evaluation (accuracy, precision, recall)
* Basics of Neural Networks
* Introduction to LLMs and Transformers

💡 **Daily Post Tip:**\
\&#xNAN;*“Here’s how \[topic] could lead to a security issue…”*

***

## 🔐 Phase 2: AI/ML Threat Landscape (Days 21–40)

**Goal:** Learn all the ways AI/ML systems can be attacked or go wrong

### Topics

* Model inversion attacks
* Membership inference attacks
* Adversarial ML (e.g., image perturbations)
* Data poisoning and backdooring
* Supply chain attacks on ML (libraries, datasets)
* Attacks on training/inference pipelines
* LLM-specific issues (prompt injection, jailbreaks, data leakage)
* Privacy risks in federated learning


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