Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T188623CB9B21401E19D4343DABB6722AEF103D1AEDA225A98E379431C76D4DFD8824DC6 |
|
CONTENT
ssdeep
|
192:QoGoBYJ5J9Ij4PTEJOTYfkGD+9WXLRkRfmku9cuGRmKbMpBXp7sfgg8gk:Q1ogxXcNa9aaxsmMpBZ7eg/B |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
baa4cd9294e5c33a |
|
VISUAL
aHash
|
ff8100840c0c0080 |
|
VISUAL
dHash
|
4d6d430d0d494949 |
|
VISUAL
wHash
|
ff8181878dcd81e3 |
|
VISUAL
colorHash
|
3a000e00000 |
|
VISUAL
cropResistant
|
4d6d430d0d494949,1fdeceaee6ced060,a9918e8d898983ca |
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 490 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)