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 ...
AI autoscaling promises a self-driving cloud, but if you don’t secure the model, attackers can game it into burning cash or ...
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 ...
Deep neural networks (DNNs) have become a cornerstone of modern AI technology, driving a thriving field of research in ...
A new report has revealed that open-weight large language models (LLMs) have remained highly vulnerable to adaptive multi-turn adversarial attacks, even when single-turn defenses appear robust. The ...
Enterprise security faces a watershed as AI tools mature from passive analytics to autonomous operatives in both offense and defense. To date, traditional ...
AI security is vital for protecting systems across industries like healthcare, finance, and media. It addresses vulnerabilities and evolving threats.
For decades, cybersecurity has been a battle of attrition — defenders patching, attackers probing, both sides locked in an ...
Red teaming is a powerful way to uncover critical security gaps by simulating real-world adversary behaviors. However, in practice, traditional red team engagements are hard to scale. Usually relying ...
Microsoft reports a multi-stage AitM phishing and BEC campaign abusing SharePoint, inbox rules, and stolen session cookies to ...
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