Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T16FA25935A60150A703B7D9C0F2657F1FB6DAF30F85068152AEBD919A2FC3CB67B60066 |
|
CONTENT
ssdeep
|
192:6mVRWlOdZ+qFpFQFEF5FvFYFBFOFVMiNhW5XZEkPcrdlAudqbTt:ZyOdZ+qFpFQFEF5FvFYFBFOFVMuEuU |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
e66666668c999999 |
|
VISUAL
aHash
|
c3c3efffffffe7ff |
|
VISUAL
dHash
|
4d0d160c0c0c0c0c |
|
VISUAL
wHash
|
03030303e3c3c7c3 |
|
VISUAL
colorHash
|
07000600010 |
|
VISUAL
cropResistant
|
4d0d160c0c0c0c0c,f9f1f0f0f2d2d251,0b3b1b071703e080 |
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 23 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)