Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T135B20C30A4449C3B1293E6E6B2312B0BA6969600DFD31B92D7F5836D1FE5D81EF67720 |
|
CONTENT
ssdeep
|
768:Rk3Zw8kvJlk3gvesUg4gU5c2kxsBMECsUg4gU5c2kxsBME34t4GmFSqsXDCkznEx:Rk3Zw8kvJlk3gvesUg4gU5c2kxsBMEC3 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
da7164ce64309b9b |
|
VISUAL
aHash
|
803c1c183c3c3c18 |
|
VISUAL
dHash
|
076979f070717171 |
|
VISUAL
wHash
|
e0bc3c7e3c3c3c18 |
|
VISUAL
colorHash
|
30006600000 |
|
VISUAL
cropResistant
|
076979f070717171 |
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 1313 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.