Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T161924235E952F867016340D976E1F34C23D6668743523B616EEA31E797CF6F8D8E8280 |
|
CONTENT
ssdeep
|
384:ju2RDYZFEyC2T2+h+4eUAT63QbolO1iDRsUL:jumEFC2T28+7bUL |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
cc66cc665ca6d519 |
|
VISUAL
aHash
|
18186e0010189999 |
|
VISUAL
dHash
|
b231c83333b23333 |
|
VISUAL
wHash
|
38383380801f9f9f |
|
VISUAL
colorHash
|
000000001c0 |
|
VISUAL
cropResistant
|
0e0fe4c6c5e4c4cc,a2a2a2a2a280a280,82a2a2a2a2a2a2a2,b231c83333b23333 |
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 2450 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.
| ID | Português | Inglês | Trigger |
|---|---|---|---|
Pages with identical visual appearance (based on perceptual hash)