Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T189C22824714191A382B39DD2E460BF3A76E3F30F80568954BFAD498A8FC7CB8BC15576 |
|
CONTENT
ssdeep
|
192:6ERRlgiYuF12pSiFlFFFEFkFfFFm0FoME3R5VxY3jLtljI9u7LvUwQwNj9hf:1y6z2pSiFlFFFEFkFfFFm0FoMd15 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b367233399999998 |
|
VISUAL
aHash
|
c3c3ffffe7ffffff |
|
VISUAL
dHash
|
0f0d060c0c0c1404 |
|
VISUAL
wHash
|
c3c3c3c3c3c3c3c3 |
|
VISUAL
colorHash
|
070000001c0 |
|
VISUAL
cropResistant
|
0f0d060c0c0c1404,800180a280e0a4e4,a201004832030169 |
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 33 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)