Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T155C266718054F43B01D792C4A625BB5EF3C3A5CACE524E81A7FC835C1FDBE94E852A29 |
|
CONTENT
ssdeep
|
192:C8hW4aratL6XYk0RFBV9uvqbf67fNUz/69JKSF+OjdH3L47t4wtVOttNBjHVcGyi:LJaGtPk0R3s4dyjz7rzv |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
ac9c966d33c36391 |
|
VISUAL
aHash
|
ffd3c3c18181ffe7 |
|
VISUAL
dHash
|
3337230f13270fcc |
|
VISUAL
wHash
|
ff8381818181efe7 |
|
VISUAL
colorHash
|
07200080003 |
|
VISUAL
cropResistant
|
3337230f13270fcc,ede9e976e9fff5fd,e294960a1b1382c2,0ca2a22c90906000 |
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 5 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)