Microsoft customers are going through a novel quishing marketing campaign that leverages weaponized QR codes embedded in malicious emails.
Rising in early October 2025, this assault exploits belief in QR-based authentication and machine pairing workflows, tricking targets into scanning codes that ship infostealer binaries.
Preliminary studies surfaced when Gen Menace Labs analysts famous anomalous QR attachments spoofing Microsoft branding inside company Workplace 365 notifications.
Recipients who scanned the codes have been redirected to a compromised Azure CDN node internet hosting a staged payload supply sequence.
Following its emergence, researchers recognized a number of an infection vectors. One entails a phishing e-mail purporting to be a Microsoft Groups alert, instructing customers to scan a QR code to resolve an pressing safety challenge.
One other impersonates a Microsoft Authenticator enrollment immediate, promising “enhanced login safety” upon scanning. As a result of many organizations encourage QR-based multi-factor setup, these lures seem reputable at first look.
Gen Menace Labs researchers famous that victims noticed acquainted Microsoft logos and accurately formatted hyperlinks, growing the marketing campaign’s attain and success fee.
The affect spans credential theft and system compromise. As soon as the QR code is scanned, victims obtain a brief URL that resolves to a malicious redirector script.
This script performs environmental checks—verifying Home windows locale, put in Defender variations, and sandbox indicators—earlier than downloading aPackaged Infostealer (PI) executable.
This binary establishes persistence by making a scheduled activity named “MSAuthSync,” guaranteeing execution at every consumer logon. Extracted credentials and host telemetry are exfiltrated over HTTPS to attacker-controlled endpoints.
An infection Mechanism
A key innovation on this quishing assault is its QR code AV evasion method. Quite than embedding a single QR picture, the malware splits the code into two overlapping photos drawn through PDF content material streams.
Normal QR decoders ignore nonstandard coloration palettes and break up segments, however the customized parser recombines picture layers earlier than decoding.
The next Python snippet illustrates how a defender would possibly reconstruct and decode such break up QR codes:-
from PIL import Picture
import zbarlight
# Load the 2 picture layers
layer1 = Picture.open(‘qr_part1.png’).convert(‘RGB’)
layer2 = Picture.open(‘qr_part2.png’).convert(‘RGB’)
# Recombine by taking the brighter pixel from every
merged = Picture.new(‘RGB’, layer1.dimension)
pixels1, pixels2 = layer1.load(), layer2.load()
for x in vary(layer1.width):
for y in vary(layer1.top):
pixels = pixels1[x, y] if sum(pixels1[x, y]) > sum(pixels2[x, y]) else pixels2[x, y]
merged.putpixel((x, y), pixels)
# Decode the merged QR code
codes = zbarlight.scan_codes(‘qrcode’, merged)
print(‘Decoded URL:’, codes[0].decode())
This strategy highlights how weaponized QR photos can evade each static AV signatures and naive visible inspections, underscoring the necessity for layered evaluation in fashionable phishing campaigns.
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