Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T15D33A830B982A8374193C1D4AF7A971B76E0F346C6434705ABF8C3AC6BEED5AED05548 |
|
CONTENT
ssdeep
|
1536:2vyA1y1s5adwBmiSj6nNLEWR2ZJZccLsDgH:2vy/cq |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b333cccccc313333 |
|
VISUAL
aHash
|
cfe7c7c7ffefefe7 |
|
VISUAL
dHash
|
104d4d0c14181c0c |
|
VISUAL
wHash
|
c3c3c7c303030303 |
|
VISUAL
colorHash
|
07200010080 |
|
VISUAL
cropResistant
|
104d4d0c14181c0c,d99655d792965654 |
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 16001 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)