LummaStealer has emerged as one of the most prolific information-stealing malware families in recent years, targeting victims across multiple industry verticals including telecommunications, healthcare, banking, and marketing.
The sophisticated malware gained widespread notoriety in early 2025 when cybercriminals extensively deployed it in coordinated campaigns worldwide.
Although law enforcement operations in May 2025 temporarily disrupted its activities, new variants have begun surfacing again, demonstrating the persistent and evolving nature of this threat.
The malware’s resurgence has prompted security researchers to develop more advanced detection methodologies capable of identifying previously unknown variants.
Unlike traditional signature-based detection systems that rely on known indicators, modern threats like LummaStealer require innovative approaches that can adapt to the malware’s evolving tactics, techniques, and procedures.
The stealer’s ability to continuously morph its delivery mechanisms and obfuscation techniques has made it particularly challenging for conventional security solutions to detect effectively.
Netskope researchers recently identified a new LummaStealer campaign and conducted an extensive technical analysis of the sample identified by hash 87118baadfa7075d7b9d2aff75d8e730.
The analysis revealed sophisticated code obfuscation techniques, advanced evasion mechanisms designed to bypass security defenses, and complex persistence mechanisms that allow the malware to maintain its foothold on infected systems.
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This comprehensive examination provides critical insights into how the malware operates and the methodologies required to combat such evolving threats.
Advanced ML-Powered Detection Framework
The detection of LummaStealer variants requires a sophisticated multi-layered approach that combines traditional static analysis with cutting-edge machine learning techniques.
Netskope’s Advanced Threat Protection platform utilizes a Cloud Sandbox environment enhanced with purpose-built ML models specifically designed to identify novel and targeted malware samples.
The system executes suspicious files in isolated Windows environments while capturing comprehensive runtime behavioral data including process trees with API calls and DLL interactions, registry modifications, file operations, and network activity patterns.
The core innovation lies in the implementation of a tree transformer architecture that analyzes the intricate patterns within malicious process trees and their associated behavioral features.
This approach employs tree positional embeddings to encode each node and its position within the execution hierarchy, creating a comprehensive understanding of the malware’s operational flow.
Runtime behavioral features such as registry modifications, file operations, and network communications are encoded into feature vectors and combined with process tree embeddings to generate final malware classifications.
The transformer-based architecture enables the detection system to capture generalized behavioral patterns rather than relying solely on specific signatures or indicators. This methodology prevents overfitting to training data while significantly enhancing the ability to detect previously unseen threats.
When analyzing the LummaStealer sample, the ML model successfully identified malicious behavior through process tree embeddings combined with suspicious runtime activities, demonstrating the effectiveness of this approach against sophisticated evasion techniques.
The analyzed sample was categorized as a Nullsoft Scriptable Install System (NSIS) installer file, which upon extraction revealed multiple components including an obfuscated NSIS script and various payload files disguised with .m4a extensions.
The malware leveraged legitimate AutoIt scripting language for malicious purposes, highlighting a common tactic where threat actors repurpose trusted system utilities to evade detection while carrying out their objectives.
[NSIS].nsi: Obfuscated NSIS script, will invoke Parish.m4a to initiate the chain
Parish.m4a: obfuscated batch file
Other *.m4a: Blobs for next stage payloadThe sophisticated evasion techniques employed by this variant initially resulted in a very low detection rate of only 9 out of 73 antivirus engines on VirusTotal, demonstrating the effectiveness of its anti-analysis mechanisms and the critical need for advanced ML-based detection approaches to identify such threats.
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