Adversarial Machine Learning
Noun · Security & Infosec
Definitions
Adversarial Machine Learning is the study of attacks and defenses involving machine-learning systems, including evasion, poisoning, and model extraction. Security teams use it to enforce trust, reduce exposure, improve detection, or standardize secure operations in production environments. Its value depends on correct configuration, lifecycle management, and surrounding controls, because weak defaults, poor integration, or missing visibility can create gaps that attackers exploit.
In plain English: Adversarial Machine Learning is a security concept or control that helps organizations protect systems, manage trust, and notice suspicious behavior before damage spreads.
Example: "After the review, the security team rolled out Adversarial Machine Learning in the affected environment, documented the operating procedure, and verified through logs and test cases that the control reduced exposure without breaking normal administrative or user workflows."
Related Terms
- Classification
- Algorithm Downgrade Attack
- Binary Exploitation
- Birthday Attack
- Blind SQL Injection
- Brute Force Attack
- Buffer Overflow Attack
- Clickjacking Attack
- Code Injection
- Deauthentication Attack
- DNS Spoofing
- Email Spoofing
- Exfiltration Channel
- Exploit Chain
- Host Header Injection
- IP Spoofing
- Kernel Exploit
- Living off the Land
- Log Injection
- Logic Bomb
- Malware
- Responsible AI Security