Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T12972AB716098223F226362C5E730FF05A3D3A285CE9799A1B3F7465D3FE5D84DD0AA24 |
|
CONTENT
ssdeep
|
384:PDOshE/OGsqsTsOsssts4O7W5i3b+K+K1KdnNpP:7O0E/5AW51Xw0R |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
9c6360181f3ec3f9 |
|
VISUAL
aHash
|
00ffff9f1f1f9fff |
|
VISUAL
dHash
|
8c84287474b43430 |
|
VISUAL
wHash
|
0000000000001010 |
|
VISUAL
colorHash
|
06000040180 |
|
VISUAL
cropResistant
|
0000000000000000,2080707434b43470,00409ccc8c984000,9113565666660f1a,7c1f3731134f3f7e,78e08d1c2fc3cb7b |
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 5 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)