Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1F7929E72760041A703F799C4E5607E5FB2DAF30F8016C511ABBE909A2FD3CB67B651A2 |
|
CONTENT
ssdeep
|
192:6TVRlCBwQffFmFbFFFwFdFOveF5FJMLbt9TFniTH3TE+XE3PpN:SU2QffFmFbFFFwFdFOmF5FJMBiNMj |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b3313131d9dccccc |
|
VISUAL
aHash
|
c3c3f7ffe7e7ffff |
|
VISUAL
dHash
|
160e0e0c0c0c0c04 |
|
VISUAL
wHash
|
c3c3c3c3c3c3c3c3 |
|
VISUAL
colorHash
|
07000000c80 |
|
VISUAL
cropResistant
|
160e0e0c0c0c0c04,10192c2818323631,5b9b93cf496d8dc9 |
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 26 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)