Explanation-driven manipulation represents a structural vulnerability in AI-assisted decision making. Attackers do not need to compromise training data, model parameters, or system infrastructure.

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 ...

Enterprise security faces a watershed as AI tools mature from passive analytics to autonomous operatives in both offense and defense. To date, traditional ...

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

Adam Stone writes on technology trends from Annapolis, Md., with a focus on government IT, military and first-responder technologies. Cybercriminal groups are leveraging artificial intelligence to ...

The rapid advancements in artificial intelligence (AI) have transformed industries across the globe, with AI chips at the ...

A bipartisan group of U.S. lawmakers introduced the No Adversarial AI Act on Wednesday in an effort to ban Chinese artificial intelligence models, such as those made by DeepSeek (DEEPSEEK), in federal ...

Faced with increasingly sophisticated multi-domain attacks slipping through due to alert fatigue, high turnover and outdated tools, security leaders are embracing AI-native security operations centers ...

SCYTHE, the leading provider of Adversarial Exposure Validation (AEV) and continuous security control testing, and Starseer, a pioneer in AI Runtime Assurance and Detection Engineering, today ...

The bill mandates the creation of a list of AI systems that are produced or developed by foreign adversaries. Key points include: The Federal Acquisition Security Council must develop this list within ...

FOSDEM 2026 confronts open-source burnout, the rise of ‘adversarial’ AI, and celebrates 30 years of KDE in one of the even's ...