Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A modern storefront can look perfectly healthy while malicious JavaScript works underneath: siphoning affiliate revenue, hijacking searches and clicks, tampering with analytics, or asking a remote server what to execute next. Pages load, products appear, and checkout works — yet the browser may be quietly doing something the site owner never authorized. That is the blind spot our Client-Side Security machine learning (ML) model is built to expose.

A backdoor attack is hard to detect. Since they bypass standard security measures, backdoor attacks can enter your system and go unnoticed for a long time. While some cyberattacks tend to be smash-and-grab, backdoor attacks are stealthier. They allow hackers to enter secretly, gather more secure data than typical attacks, and can cause significant damage. Because they can be so difficult to detect and cause so much damage, you need to know the signs of a backdoor attack and learn how to mitigate it.

An employee at your company receives an email from [email protected]. The domain matches. There is no warning banner. The message asks them to review a payment approval document. They click. That email was never sent by your HR team. An attacker sent it, and your own Microsoft email infrastructure delivered it, with no password and no credentials required.

With most cyberattacks, the problem announces itself. A service goes down. A user cannot log in. An alert fires. Denial-of-wallet is different. The application keeps working normally while the cost of running it climbs in the background. That is what makes it a security problem and not only a budgeting one. The goal is not to take a system offline. It is to make the system do expensive work that nobody asked for.

Researchers at ZeroBEC are tracking a phishing platform called “Greatness” that’s been significantly upgraded since it surfaced in 2023. “Since its initial discovery, the platform has evolved significantly,” the researchers write.

The most damaging cyber attacks in history rarely involved exotic techniques. A graduate student's experimental worm, a password with no second factor, and an unpatched file transfer tool each caused more damage than any advanced exploit on record. Reading four decades of these incidents in order shows how consistently intruders take the simplest available route, and how little that route has changed since 1988.

The volume of published incident research involving agentic AI is increasing, and the analysis that follows each report tends to concentrate on the same attribute: speed. The recent investigation from Unit 42, the threat intelligence and incident response group at Palo Alto Networks, is a representative case.

The tokenmaxxing era has left companies grappling with an uncomfortable reality. Now that AI vendors have switched to usage-based billing models, businesses are facing sky-high bills, and IT and finance teams are under pressure to rein in spending without slowing down innovation.