Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T18A725235A2B0D235503FB19FF621AB14D09BE34BC79247F19AB581640AE2DEAFD4F108 |
|
CONTENT
ssdeep
|
192:gKzjUdtv8xFTPhl3Ld4xeYU+L2v3o9AnfmvnV3T55N7V1ty3L3ZtZVffrbV+A:b+8xFTPhlSxI370nLNy3LJtZVrbYA |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
e964656965657992 |
|
VISUAL
aHash
|
c3c3ffc3ffcbffff |
|
VISUAL
dHash
|
96161e96161a0200 |
|
VISUAL
wHash
|
8181c3c3c3c3c3ff |
|
VISUAL
colorHash
|
070000005c0 |
|
VISUAL
cropResistant
|
96161e96161a0200,00c121212120c100,00803048c8c02000,000020b2b2300c00 |
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 6 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)