Adversarial machine learning exploits vulnerabilities in AI-enabled security systems by crafting inputs that appear normal but cause misclassifications. Attackers use these techniques to deceive malware detectors and intrusion systems, allowing malicious activity to go undetected.
This undermines trust in cybersecurity defenses and complicates detection, increasing risk. To counter these threats, organizations must adopt robust training methods like adversarial training and improve AI explainability to strengthen their machine learning models.