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SUMMARY:Comprehensive Framework for Analyzing and Detecting Malicious Brow
 ser Extensions - Van Nguyen
DTSTART;TZID=Europe/Luxembourg:20260508T094000
DTEND;TZID=Europe/Luxembourg:20260508T101000
DTSTAMP:20260911T043955Z
UID:pretalx-bsidesluxembourg-2026-MB9KND@pretalx.com
DESCRIPTION:Every day\, millions of people rely on their web browsers\, no
 t only for work but also for study and daily life. Some of us also install
  browser extensions to utilize useful features. But what happens when thos
 e extensions are not as harmless as they seem?\n\nIn recent years\, there 
 has been a growing number of malicious browser extensions\, particularly o
 n platforms like the Chrome Web Store (CWS)\, affecting millions of users 
 worldwide. Detecting these threats is not straightforward. Malicious exten
 sions behave in many different and sometimes unpredictable ways. Another c
 hallenge is the limited availability of corresponding known malware sample
 s\, which restricts our ability to investigate these threats in depth. \n
 \nIn this talk\, I will share insights from my study that takes a closer l
 ook at this problem. I compiled a curated dataset of 460 malicious browser
  extensions removed from the CWS and analyzed how they behave. By integrat
 ing both static and dynamic analysis techniques\, I identified a wide rang
 e of activities that raise privacy and security concerns\, classified as t
 racking\, redirecting\, ad injecting\, stealing\, and unwanted actions. Le
 veraging static analysis using CodeQL and Python\, the study could detect 
 extensions setting cookies for external domains automatically.
LOCATION:Building D/room 1\, Workshops and Detection Engineering village
URL:https://pretalx.com/bsidesluxembourg-2026/talk/MB9KND/
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