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 ...

Your security tools say everything’s fine, but attackers still get through. Despite years of investment in firewalls, endpoint protection, SIEMs, and other layered defenses, most organizations still ...

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 ...

The Department of War’s shift to “Operational Speed” is a signal to the private sector: The age of the checklist is over.

The Tidal Cyber 2025 Threat-Led Defense Report represents a groundbreaking shift in cybersecurity analysis by placing real adversary behavior at the forefront of defense strategies. Read the Full ...

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 ...

"An AI system can be technically safe yet deeply untrustworthy. This distinction matters because satisfying benchmarks is necessary but insufficient for trust." ...

Adversaries weaponized recruitment fraud to steal cloud credentials, pivot through IAM misconfigurations, and reach AI ...

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 ...

After 30 years in cyber defense and research, I joined AttackIQ to bring clarity and prioritize what truly matters in security. The post Why I Chose to Join AttackIQ as a Senior Advisor appeared first ...