Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1CC43F271720536B7057BB5C06C21AB89A0C2D766C1138185AAFDA1220FC7FF2FF5A5B9 |
|
CONTENT
ssdeep
|
192:uMG2I+fTfrTTiTyT6AKezTs7Ux4GxXjZFu5NY9ch9ke3lLM0d5cknRZhBYlQTOz7:zFfTfH3L2jY9+XwkRZX0yUOOY/eOyxh/ |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
96942ccd6873da9c |
|
VISUAL
aHash
|
1f3f3e7e7c380000 |
|
VISUAL
dHash
|
fefefcc8f0e0c136 |
|
VISUAL
wHash
|
1f3f3f7e7e380000 |
|
VISUAL
colorHash
|
080020001c0 |
|
VISUAL
cropResistant
|
fdfcfcf898e4c0d0,fefefcc8f0e0c136 |
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 16 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)