Adversarial Machine Learning (NIST)

Taxonomy: Organizes attacks by stage (training vs. deployment), objective (availability, integrity, privacy), and attacker knowledge (white-box, gray-box, black-box).

Method catalog: Systematically reviews evasion, poisoning, extraction, and inference attacks with representative examples and current defenses.

Mitigation landscape: Evaluates robustness techniques (adversarial training, certified defenses, monitoring) alongside their practical limitations.

Standards role: As a NIST publication, this document is likely to shape how U.S. agencies and regulated industries describe and defend against adversarial ML attacks.