Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T14C83B6719240A97740E383E1B675BB9B72D1C24ACB434B6186FCCB9DDBE6DA0CC3A454 |
|
CONTENT
ssdeep
|
1536:DmdWcWYDjKaqz8qCIUokRglQAqAAYfkdEJvuy0OSGPlCX:0Dj9EJvu9X |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
bc916ac96ec33269 |
|
VISUAL
aHash
|
fd9f8f8f9bbfc181 |
|
VISUAL
dHash
|
6b3c383c3332032f |
|
VISUAL
wHash
|
f98f8f87899b8181 |
|
VISUAL
colorHash
|
07000000180 |
|
VISUAL
cropResistant
|
6b3c383c3332032f,82e0ea82b0b880f0,e6662815348eb244,b7b39133358ca966,21de21a6a636b6a6 |
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 3502 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)