Threats Tagged 'machine learning'

This analysis focuses on a new variant of Lumma Stealer, a malware that reemerged after a brief hiatus following a law enforcement operation. The article details the malware's code obfuscation, evasion techniques, and persistence mechanisms. It describes Netskope's machine learning-based detection approach, which utilizes a Cloud Sandbox enhanced with ML models to analyze runtime behavior, process trees, and other features. The specific sample analyzed is an NSIS installer file that abuses AutoIt for malicious purposes. The malware employs various anti-analysis techniques and establishes persistence through the Windows Startup folder. Netskope's multi-layered threat protection system successfully detected this Lumma Stealer variant.

MediumMalwareGermanyFranceUnited Kingdom+4#evasion techniques#sandbox#anti-analysis

A new malicious campaign has been discovered targeting the Python Package Index (PyPI) by exploiting the Pickle file format in machine learning models. Three malicious packages posing as an Alibaba AI Labs SDK were detected, containing infostealer payloads hidden inside PyTorch models. The packages exfiltrate information about infected machines and .gitconfig file contents. This attack demonstrates the evolving threat landscape in AI and machine learning, particularly in the software supply chain. The campaign likely targeted developers in China and highlights the need for improved security measures and tools to detect malicious functionality in ML models.

MediumMalwareGermanyFranceNetherlands+5#pytorch#pypi#pickle format