Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T107C34F78231C3E2DA51B8AE4F7A5FB69132C9190F95FD0A892BC563117CBC84F8279D4 |
|
CONTENT
ssdeep
|
1536:Ww5NEwy0000eu0000GD0000g+0000o40000Yk0000Hd0000eH0000rO0000xT009:PCwP9 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
8909e2ce0cddf535 |
|
VISUAL
aHash
|
3800000000ffffff |
|
VISUAL
dHash
|
f6bb92b2b600020c |
|
VISUAL
wHash
|
fe00000800ffffff |
|
VISUAL
colorHash
|
0b200008180 |
|
VISUAL
cropResistant
|
62e0e0629c2c0093,d0d696a626364482,a4a9ab4b9b9b2b6b,8a00000000000c0c,b2b2720c62e0e4e2,9bfb969a92b2f28e |
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 63 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)