Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1A2420030A1C8393B45A35EDAF074672EA6D7C20FCB4765A2E2FC97A90FF5C84D651818 |
|
CONTENT
ssdeep
|
384:NxBYzYVCX7H4a+FedBNt+NeTAQPO1dMYMOT6XTf3:NxuzYVCXE/EC1aYDT6XTf3 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b4eb4514c934e3e3 |
|
VISUAL
aHash
|
ff00ffffff000000 |
|
VISUAL
dHash
|
c1310d2a23d49484 |
|
VISUAL
wHash
|
ff00ffffff000000 |
|
VISUAL
colorHash
|
07038000000 |
|
VISUAL
cropResistant
|
0000000409090400,09394c2c32323041,8aaeeeecaaabaaaa,0000000000000000,00402cd2d22c8200,2cd494e4a4948443 |
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 12 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)