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 integration of deep learning techniques into wireless communication systems has catalysed notable advancements in tasks such as modulation classification and spectrum sensing. However, the ...

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 ...

Adversaries are unleashing new tradecraft to exploit any weakness they can find in endpoints, relying on generative AI (gen AI) to create new attack weapons of choice. Cybersecurity teams who have ...

SAN FRANCISCO, March 19, 2026 (GLOBE NEWSWIRE) -- Votal AI, the AI-native security platform purpose-built for agentic AI systems and founded by cybersecurity veterans Bobby Gupta (CEO) and Jyotirmoy ...

The easy availability of highly effective phishing-as-a-service platforms has ensured the technique's ongoing relevancy despite its provenance dating to the internet's early days. See Also: Why ...

BreachLock, a global leader in offensive security, today announced it has been named a representative vendor ...

The Splunk Threat Research Team is releasing v4.0 of Splunk Attack Range, an open source project that allows security teams to spin up a detection development environment to emulate adversary behavior ...

IFAP generates adversarial perturbations using model gradients and then shapes them in the discrete cosine transform (DCT) domain. Unlike existing frequency-aware methods that apply a fixed frequency ...

Stolen credentials turn authentication systems into the attack surface. Token shows how wearable biometric authentication ...