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 Artificial Intelligence and Machine Learning (“AI/ML”) risk environment is in flux. One reason is that regulators are shifting from AI safety to AI innovation approaches, as a recent DataPhiles ...

NIST’s National Cybersecurity Center of Excellence (NCCoE) has released a draft report on machine learning (ML) for public comment. A Taxonomy and Terminology of Adversarial Machine Learning (Draft ...

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 ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Security leaders’ intentions aren’t matching up with their actions to ...

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 ...

AI red teaming — the practice of simulating attacks to uncover vulnerabilities in AI systems — is emerging as a vital security strategy. Traditional red teaming focuses on simulating adversarial ...

Facepalm: Machine learning algorithms are the foundation of well-known products like OpenAI's ChatGPT, and people are using these new AI services to ask the weirdest things. Commercial chatbots should ...

Samer Khamaiseh, assistant professor of Computer Science and Software Engineering, directs and leads the Laboratory of A.I. Security Research (LAiSR) at Miami University’s College of Engineering and ...