Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...

The National Institute of Standards and Technology (NIST) has published its final report on adversarial machine learning (AML), offering a comprehensive taxonomy and shared terminology to help ...

The final guidance for defending against adversarial machine learning offers specific solutions for different attacks, but warns current mitigation is still developing. NIST Cyber Defense The final ...

Threat actors can hijack machine learning (ML) models that power artificial intelligence (AI) to deploy malware and move laterally across enterprise networks, researchers have found. These models, ...

“My work protects millions of users, translating theoretical research into practical security implementations at scale.” ...

Adversarial AI exploits model vulnerabilities by subtly altering inputs (like images or code) to trick AI systems into misclassifying or misbehaving. These attacks often evade detection because they ...

In a landmark move, the US National Institute of Standards and Technology (NIST) has taken a new step in developing strategies to fight against cyber-threats that target AI-powered chatbots and ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...