Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T12C346C73B22453A7910B87C5F8A76516B66D20FF69460DD0B318CDE4A36CC9EA4B3EC1 |
|
CONTENT
ssdeep
|
1536:v01HMlR3qNKP3xfoma626INK+XJhMsDuNNK/WoUTbNKlNKvNKUB3lkKunv//QKuL:1lOhMsy6iJtiNkDShvOsN50Tstbq |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
96956c69626b499b |
|
VISUAL
aHash
|
263e1e243e000434 |
|
VISUAL
dHash
|
ccfcfcccccaacccc |
|
VISUAL
wHash
|
267e3e3e7e240474 |
|
VISUAL
colorHash
|
30203000048 |
|
VISUAL
cropResistant
|
3b568692a3b39391,d8d2c6e9fefefefc,ccfcfcccccaacccc |
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 64 techniques to evade detection by security scanners and make reverse engineering more difficult.
Drainer scans for high-value tokens (USDT, USDC, SOL, memecoins) and prioritizes draining based on USD value. Low-value tokens are ignored to optimize transaction costs.
Pages with identical visual appearance (based on perceptual hash)