Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T150142CF66394A6F76203CFE4E532A769B14671BEEF55C54882FC16A563D2CCCC889C80 |
|
CONTENT
ssdeep
|
6144:vcCiBBtrg2pGmU4xJKXccYa0JKyK7KuKPK6KtKRKMKwKFK6KYKgKiKuKyK2KmKtr:xpLrA |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
e6263399cccc3333 |
|
VISUAL
aHash
|
e7e7e7e7e7e7e7e7 |
|
VISUAL
dHash
|
4d4d4d4d4d4d4d4d |
|
VISUAL
wHash
|
0000000000000000 |
|
VISUAL
colorHash
|
0e600018000 |
|
VISUAL
cropResistant
|
0809090809090809,c0d0c0c0d0d0d0c0,67f43baaa4f4eb2b |
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 7079 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)