On November’s Patch Tuesday, Microsoft fixed 63 vulnerabilities , including a critical zero-day vulnerability that had already been exploited in attacks. This time, the fixes affected a wide range of Windows components and Microsoft products, from the operating system kernel to the Office suite and cloud solutions. According to the company itself, the most dangerous vulnerability affected the Windows kernel and allowed for the escalation of system privileges; the vulnerability has been assigned the identifier CVE-2025-62215. The flaw allowed local privilege escalation by exploiting incorrect synchronization during resource sharing . The bug was discovered by Microsoft’s internal threat intelligence team. Of the

Flame, (also known as Flamer, sKyWIper, Skywiper), is a modular computer malware discovered in 2012 that attacked Middle Eastern target computers running the Microsoft Windows operating system. The program’s primary purpose was cyber espionage, aimed at gathering intelligence in Middle Eastern countries. Russian cybersecurity firm Kaspersky Labs told the BBC that it believes the malware, known as Flame, has been operational since August 2010, so it’s likely that its development occurred very close to the time the Stuxnet malware was written. The malware is highly sophisticated, though unconfirmed, and appears to have been written by US-sponsored experts in conjunction with the Israeli

Many years have passed since the SQL Slammer worm spread uncontrollably on January 25, 2003. It was one of the fastest-spreading malware in history , and with this article we want to retrace those moments, to understand technically how it worked and what happened that day many years ago. At that time, cybersecurity concepts were not as widespread as they are today, and many people found themselves completely unprepared and disoriented in dealing with this cyber pandemic. What is SQL Slammer? If you worked in IT in 2003, you’ll remember what you were doing when Slammer entered your life. It was a

We all know Bruce Schneier , a renowned cryptography and computer security expert, member of the board of directors of the International Association for Cryptological Research and a renowned speaker, who famously said the phrase at the RSA conference a few years ago: “the business model of the Internet is surveillance.” But on October 12, 2015, Bruce Schneier revealed this interesting story that we want to tell you today, the story of the first keylogger. The first keylogger in history The first keylogger in history appeared during the Cold War, in the 1970s, when Soviet intelligence officers spied on American diplomats by

At first we talked about “viruses” , then “worms” appeared, followed by “macro viruses”. These were soon joined by other types of hostile software such as keyloggers or lockers. At some point we all started calling them malware more generically. And just like biological viruses, malware has evolved over time; some are highly opportunistic, emerging to exploit short-term opportunities, while others have evolved to exploit more fundamental flaws and problems in IT systems that have not yet been fixed. From Creepers to Modern Ransomware The first viruses in computing history date back to the 1970s and 1980s. The first piece of malware

In the rapidly evolving world of artificial intelligence (AI), one term has emerged with increasing importance: Language Model, especially large language models, then called Large Language Models. You’ve likely already used tools like chatGPT, deep learning models that generate coherent, human-like text. If so, you’ve already experienced the capabilities of large language models. In this article, we’ll delve deeper into what language models are, how they work, and explore some important examples from the scientific literature. Linguistic Models In short, a language model is software that can predict the probability of a sequence of words . Language models are trained on large

In the world of artificial intelligence, we often hear two terms: machine learning (ML) and deep learning (DL). Both are methods for implementing AI through the training of machine learning algorithms, which are then used to make predictions about the future and make decisions. These two subjects use different approaches and have different capabilities and characteristics. In this article, we’ll explore the differences between the two and better understand how they work. Machine Learning Machine learning is the discipline of designing algorithms capable of making predictions by learning patterns from past data. A machine learning algorithm differs from a “classic” computer science

Artificial intelligence isn’t about magic, it’s about learning! This article aims to demystify the esotericism surrounding artificial intelligence (AI) by providing a comprehensive answer to the question , “How do machines learn?” Indeed, the ” magic ” behind AI’s functioning lies in the learning phase. Artificial intelligence applications use vast amounts of data, from which patterns are identified to make data-driven decisions. There are several approaches to learning, including supervised , unsupervised , and reinforcement learning. These methods differ in their objectives and problems to solve, as well as in the type of data available: labeled examples, unlabeled examples, or through direct

In the digital age we live in, artificial intelligence (AI) is gaining increasing attention. But what distinguishes a classical algorithm from AI software? In this article, we’ll explore the differences between these two concepts in a simple and accessible way, even for those who aren’t computer experts. Classical algorithm A classical algorithm can be described as a series of precise, sequential instructions that a computer follows to solve a given problem. For example, imagine a classical algorithm for sorting a list of numbers in ascending order. One of the classical (but inefficient) methods we study in college is called “Bubble Sort,” which

In the contemporary technological landscape, one term stands out: neural network. The foundation of artificial intelligence, neural networks represent the future of how we interact with technology. But what exactly is a neural network? Let’s start our journey. Definition of a neural network A neural network is a computational model inspired by the functioning of the human brain . This similarity is not limited to a simple metaphor: just as our brain is composed of interconnected neurons, a neural network is composed of computing units, or “artificial neurons,” organized into different layers. Each neuron receives input data, performs a series of calculations