Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T18F52E530B02214B741A79DC4A5A0BF6F35E6E38FC28F91811BB98B590FD7DB5FA540A1 |
|
CONTENT
ssdeep
|
384:YHo0QPiuKc0D0IF266DbF5DdDKF7wXDrVfJbDpFqFr1AtZwblwGU:+o0QPiuKc0DFF266DbF5DdDKF7wXDrVZ |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
c16e3ec5c3b62439 |
|
VISUAL
aHash
|
0000007e7e7e0000 |
|
VISUAL
dHash
|
e4e2d4d8d0e0d415 |
|
VISUAL
wHash
|
3000007e7e7effa5 |
|
VISUAL
colorHash
|
11007000000 |
|
VISUAL
cropResistant
|
0100595971614555,e4e2d4d8d0e0d415 |
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 606 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)