Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T18282B62512582E7E253387D5F3E5B3755259D28BD32EC22AE57C03712682ED8D833ED8 |
|
CONTENT
ssdeep
|
384:v53AmoRX9PY+0tcFoJOrctCvQsOyvuvUvHvrrvb4:vamoRX9PYEFoJOgtCoryvuvUvHvvvb4 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
bb137dc66949c0e4 |
|
VISUAL
aHash
|
00040000ffffffc3 |
|
VISUAL
dHash
|
1b5c5c1b1f842b2b |
|
VISUAL
wHash
|
00060600ffffffc3 |
|
VISUAL
colorHash
|
0b000000006 |
|
VISUAL
cropResistant
|
a280829a9a8280a2,18e42727cc2b232b,981a195c5c1c181a,4d3228aaaaaae869 |
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 22 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)