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Cybersecurity firms Sophos and ReversingLabs on Monday jointly released the first-ever production-scale malware research dataset to be made available to the general public that aims to build effective defenses and drive industry-wide improvements in security detection and response. " SoReL-20M " (short for  So phos- Re versing L abs –  20   M illion), as it's called, is a dataset containing metadata, labels, and features for 20 million Windows Portable Executable (.PE) files, including 10 million disarmed malware samples, with the goal of devising machine-learning approaches for better malware detection capabilities. "Open knowledge and understanding about cyber threats also leads to more predictive cybersecurity," Sophos AI group said. "Defenders will be able to anticipate what attackers are doing and be better prepared for their next move." Accompanying the release are a set of  PyTorch  and  LightGBM -based machine learning  models pre-trained...

There’s been a great deal of AI hype recently, but that doesn’t mean the robots are here to replace us. This article sets the record straight and explains how businesses should approach AI. From musing about self-driving cars to fearing AI bots that could destroy the world, there has been a great deal of AI hype in the past few years. AI has captured our imaginations, dreams, and occasionally, our nightmares. However, the reality is that AI is currently much less advanced than we anticipated it would be by now. Autonomous cars, for example, often considered the poster child of AI's limitless future, represent a narrow use case and are not yet a common application across all transportation sectors. In this article, we de-hype AI, provide tools for businesses approaching AI and share information to help stakeholders educate themselves.  AI Terminology De-Hyped AI vs. ML AI (Artificial Intelligence) and ML (Machine Learning) are terms that are often used interchangeably, but the...

Cybersecurity researchers have uncovered nearly two dozen security flaws spanning 15 different machine learning (ML) related open-source projects. These comprise vulnerabilities discovered both on the server- and client-side, software supply chain security firm JFrog said in an analysis published last week. The server-side weaknesses "allow attackers to hijack important servers in the organization such as ML model registries, ML databases and ML pipelines," it said . The vulnerabilities, discovered in Weave, ZenML, Deep Lake, Vanna.AI, and Mage AI, have been broken down into broader sub-categories that allow for remotely hijacking model registries, ML database frameworks, and taking over ML Pipelines. A brief description of the identified flaws is below - CVE-2024-7340 (CVSS score: 8.8) - A directory traversal vulnerability in the Weave ML toolkit that allows for reading files across the whole filesystem, effectively allowing a low-privileged authenticated user to es...

Cybersecurity researchers are warning about the security risks in the machine learning (ML) software supply chain following the discovery of more than 20 vulnerabilities that could be exploited to target MLOps platforms. These vulnerabilities, which are described as inherent- and implementation-based flaws, could have severe consequences, ranging from arbitrary code execution to loading malicious datasets. MLOps platforms offer the ability to design and execute an ML model pipeline, with a model registry acting as a repository used to store and version-trained ML models. These models can then be embedded within an application or allow other clients to query them using an API (aka model-as-a-service). "Inherent vulnerabilities are vulnerabilities that are caused by the underlying formats and processes used in the target technology," JFrog researchers said in a detailed report. Some examples of inherent vulnerabilities include abusing ML models to run code of the attacker...

Earlier this month, The Hacker News covered a story on research revealing how over 1300 Android apps are collecting sensitive data even when users have explicitly denied the required permissions. The research was primarily focused on how app developers abuse multiple ways around to collect location data, phone identifiers, and MAC addresses of their users by exploiting both covert and side channels. Now, a separate team of cybersecurity researchers has successfully demonstrated a new side-channel attack that could allow malicious apps to eavesdrop on the voice coming out of your smartphone's loudspeakers without requiring any device permission. Abusing Android Accelerometer to Capture Loudspeaker Data Dubbed Spearphone , the newly demonstrated attack takes advantage of a hardware-based motion sensor, called an accelerometer, which comes built into most Android devices and can be unrestrictedly accessed by any app installed on a device even with zero permissions. An ...

In December 2024, the popular Ultralytics AI library was compromised, installing malicious code that hijacked system resources for cryptocurrency mining. In August 2025 , malicious Nx packages leaked 2,349 GitHub, cloud, and AI credentials. Throughout 2024, ChatGPT vulnerabilities allowed unauthorized extraction of user data from AI memory. The result: 23.77 million secrets were leaked through AI systems in 2024 alone, a 25% increase from the previous year. Here's what these incidents have in common: The compromised organizations had comprehensive security programs. They passed audits. They met compliance requirements. Their security frameworks simply weren't built for AI threats. Traditional security frameworks have served organizations well for decades. But AI systems operate fundamentally differently from the applications these frameworks were designed to protect. And the attacks against them don't fit into existing control categories. Security teams followed the f...

Searchable Encryption has long been a mystery. An oxymoron. An unattainable dream of cybersecurity professionals everywhere. Organizations know they must encrypt their most valuable, sensitive data to prevent data theft and breaches. They also understand that organizational data exists to be used. To be searched, viewed, and modified to keep businesses running. Unfortunately, our Network and Data Security Engineers were taught for decades that you just can’t search or edit data while in an encrypted state. The best they could do was to wrap that plaintext, unencrypted data within a cocoon of complex hardware, software, policies, controls, and governance. And how has that worked to date? Just look at the T-Mobile breach, the United Healthcare breach, Uber, Verizon, Kaiser Foundation Health Plan, Bank of America, Prudential… and the list goes on. All the data that was stolen in those breaches remained unencrypted to support day-to-day operations. It’s safe to conclude that the way we...

In cybersecurity, the line between a normal update and a serious incident keeps getting thinner. Systems that once felt reliable are now under pressure from constant change. New AI tools, connected devices, and automated systems quietly create more ways in, often faster than security teams can react. This week’s stories show how easily a small mistake or hidden service can turn into a real break-in. Behind the headlines, the pattern is clear. Automation is being used against the people who built it. Attackers reuse existing systems instead of building new ones. They move faster than most organizations can patch or respond. From quiet code flaws to malware that changes while it runs, attacks are focusing less on speed and more on staying hidden and in control. If you’re protecting anything connected—developer tools, cloud systems, or internal networks—this edition shows where attacks are going next, not where they used to be. ⚡ Threat of the Week Critical Fortinet Flaw Comes Under...