Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T18AC212F3D85923BB0A5739C2280BBA6BE7E1617CCF24284173FD5139DB25F4AA4B0156 |
|
CONTENT
ssdeep
|
384:xzvGvgBRPQwT6wxkLCNOMXJymR//gW+78Q7GY6MEI2ELy6t0S2uWQEOaVhiPQjA4:xvNxc1ZsWYfdx |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b1e64717e4316659 |
|
VISUAL
aHash
|
ffffffffff000000 |
|
VISUAL
dHash
|
cc001e1c16c8d818 |
|
VISUAL
wHash
|
e7ffffff03000000 |
|
VISUAL
colorHash
|
060000001c0 |
|
VISUAL
cropResistant
|
0e060816161e1616,d8599999a1d3932c,0000000000000000,22dcdcdc22502c0c,00c4ccd8d9590410 |
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)