ML Security Protecting Models from Adversarial Attacks

Models don’t fail by accident-they’re made to fail. From adversarial images that fool vision systems to poisoned datasets, stolen weights, and prompt-level jailbreaks, attackers are now targeting ML itself. ML Security: Protecting Models from Adversarial Attacks is your practical field guide to building models that fight back.

Inside, you’ll move beyond theory to a hands-on playbook for threat-modeling ML systems, hardening training data, and defending inference at the edge and in the cloud. You’ll learn how to spot manipulation early, design robust training pipelines, validate defenses with red-team exercises, and operate with the right monitoring, logging, and rollback plans-so incidents become lessons, not headlines.

What you’ll be able to do:

Map real attacker paths: evasion, poisoning, model theft, inversion, membership inference, and LLM-style prompt attacks

Build layered defenses: input sanitization, OOD detection, certified/robust training, ensembles, and runtime guards

Protect the pipeline: data provenance, supply-chain checks, reproducible builds, secrets handling, and access controls

Safeguard privacy: differential privacy, secure aggregation, and safe release of models/embeddings

Test like an attacker: fuzzing, adaptive adversaries, canary data, watermarking, and continuous red-teaming

Operate safely: drift/attack telemetry, alerting, playbooks, and fast, reversible rollouts

Written for ML engineers, security teams, and product leaders, this book makes complex attacks understandable-and resilient defenses achievable-without hype or hand-waving.