Cyber News & Articles

Security failures rarely arrive loudly. They slip in through trusted tools, half-fixed problems, and habits people stop questioning. This week’s recap shows that pattern clearly.
Attackers are moving faster than defenses, mixing old tricks with new paths. “Patched” no longer means safe, and every day, software keeps becoming the entry point.
What follows is a set of small but telling signals.

Security failures rarely arrive loudly. They slip in through trusted tools, half-fixed problems, and habits people stop questioning. This week’s recap shows that pattern clearly.
Attackers are moving faster than defenses, mixing old tricks with new paths. “Patched” no longer means safe, and every day, software keeps becoming the entry point.
What follows is a set of small but telling signals.

Security failures rarely arrive loudly. They slip in through trusted tools, half-fixed problems, and habits people stop questioning. This week’s recap shows that pattern clearly.
Attackers are moving faster than defenses, mixing old tricks with new paths. “Patched” no longer means safe, and every day, software keeps becoming the entry point.
What follows is a set of small but telling signals.

Security failures rarely arrive loudly. They slip in through trusted tools, half-fixed problems, and habits people stop questioning. This week’s recap shows that pattern clearly.
Attackers are moving faster than defenses, mixing old tricks with new paths. “Patched” no longer means safe, and every day, software keeps becoming the entry point.
What follows is a set of small but telling signals.

If there’s a constant in cybersecurity, it’s that adversaries are always innovating. The rise of offensive AI is transforming attack strategies and making them harder to detect. Google’s Threat Intelligence Group, recently reported on adversaries using Large Language Models (LLMs) to both conceal code and generate malicious scripts on the fly, letting malware shape-shift in real-time to evade

If there’s a constant in cybersecurity, it’s that adversaries are always innovating. The rise of offensive AI is transforming attack strategies and making them harder to detect. Google’s Threat Intelligence Group, recently reported on adversaries using Large Language Models (LLMs) to both conceal code and generate malicious scripts on the fly, letting malware shape-shift in real-time to evade

If there’s a constant in cybersecurity, it’s that adversaries are always innovating. The rise of offensive AI is transforming attack strategies and making them harder to detect. Google’s Threat Intelligence Group, recently reported on adversaries using Large Language Models (LLMs) to both conceal code and generate malicious scripts on the fly, letting malware shape-shift in real-time to evade

The North Korean threat actor known as Konni has been observed using PowerShell malware generated using artificial intelligence (AI) tools to target developers and engineering teams in the blockchain sector.
The phishing campaign has targeted Japan, Australia, and India, highlighting the adversary’s expansion of the targeting scope beyond South Korea, Russia, Ukraine, and European nations, Check

The North Korean threat actor known as Konni has been observed using PowerShell malware generated using artificial intelligence (AI) tools to target developers and engineering teams in the blockchain sector.
The phishing campaign has targeted Japan, Australia, and India, highlighting the adversary’s expansion of the targeting scope beyond South Korea, Russia, Ukraine, and European nations, Check

The North Korean threat actor known as Konni has been observed using PowerShell malware generated using artificial intelligence (AI) tools to target developers and engineering teams in the blockchain sector.
The phishing campaign has targeted Japan, Australia, and India, highlighting the adversary’s expansion of the targeting scope beyond South Korea, Russia, Ukraine, and European nations, Check