Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T10252C6B16108593F12938BD4DB326A1AB38AD226FB6347A556FDC3B90ED3DE4ED53010 |
|
CONTENT
ssdeep
|
192:Fmr1BPMnq1NcqDBWOWkxA5ZrgxXSgK84FwXkuD+qdFHCeCkg+n3mjvK:Fmr1BtT1DBXoZrgxXPKnOkmLin+3v |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
e336491c69565c6b |
|
VISUAL
aHash
|
00ffe7e7ffffffc9 |
|
VISUAL
dHash
|
d0084c4d0e002b2b |
|
VISUAL
wHash
|
00e7e7e7e7ff0000 |
|
VISUAL
colorHash
|
070000001c0 |
|
VISUAL
cropResistant
|
080c4d4d0a002b2b,004020d4d4d42000,0008303232100800 |
Victim enters username and password into fake login form. Credentials are captured via JavaScript and exfiltrated to attacker's server in real-time.
Malicious code is obfuscated using 4 techniques to evade detection by security scanners and make reverse engineering more difficult.
Drainer supports multiple blockchain networks and checks for high-value tokens on each chain before executing drain operations.
Pages with identical visual appearance (based on perceptual hash)