Fabbaloo

Malware Campaigns Targets 3D Model Sites via Blender

It turns out that some participants post .blend files, which are used by the popular Blender open-source 3D modeling system.
PCMag

This Preinstalled Android Malware Can Hack Any App You Launch on a Device

Be wary of cheap Android devices. Antivirus provider Kaspersky discovers a new backdoor called 'Keenadu,' which has been ...

North Korean Graphalgo Campaign Uses Fake Job Tests to Spread Malware Scam

A North Korean attack group is running a scam operation called the Graphalgo, wherein they use fake job schemes to deliver malware.
IEEE

MiniVHS-22: A Realistic Multi-Source Dataset for Intelligent Malware Detection Using Machine Learning

Abstract: The rapid growth of malware and cyber threats demands intelligent methods for network traffic analysis. While machine learning (ML) techniques have shown high potential, their success is ...
The Hacker News

Microsoft Discloses DNS-Based ClickFix Attack Using Nslookup for Malware Staging

Microsoft details a new ClickFix variant abusing DNS nslookup commands to stage malware, enabling stealthy payload delivery and RAT deployment.
Security Boulevard

Hackers Use LLM to Create React2Shell Malware, the Latest Example of AI-Generated Threat

Darktrace researchers say hackers used AI and LLMs to create malware to exploit the React2Shell vulnerability to mine ...
Technobezz on MSN

Microsoft warns that Python infostealers now target macOS at scale

Microsoft warns that Python-based infostealers are increasingly targeting macOS, harvesting sensitive data and challenging ...
HealthcareInfoSecurity

AI-Generated Malware Exploits React2Shell for Tiny Profit

Security researchers detected artificial intelligence-generated malware exploiting the React2Shell vulnerability, allowing ...
Microsoft

Infostealers without borders: macOS, Python stealers, and platform abuse

How modern infostealers target macOS systems, leverage Python‑based stealers, and abuse trusted platforms and utilities to ...
IEEE

Real-Time Detection of Fileless Malware Using Hybrid Deep Learning with CNN and Autoencoder Anomaly Detection

Abstract: Fileless malware represents a rising and complex challenge in cybersecurity, primarily due to its capability to avoid conventional detection strategies by operating entirely within a ...