Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1A3E30C71D655613B023389E4A4722F4FB2D7F31ECA978900A7FC43D96FEBC95AA04486 |
|
CONTENT
ssdeep
|
1536:YqUAc2vxRcHS9zP9rQVW3UT6CLyMrGc9sPtM4pblAj2xWQ7QEA1pX+AYFsYT4ew0:YA2sxWQ7QEA1pX+AYFsYT4ewB2 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
f6a51add62a80376 |
|
VISUAL
aHash
|
00ff77c3e6feff00 |
|
VISUAL
dHash
|
c56cec8f4c48352d |
|
VISUAL
wHash
|
00ff6641e6fed700 |
|
VISUAL
colorHash
|
06e00008000 |
|
VISUAL
cropResistant
|
4d6cec8f4c482535,0000009393820080,010c686969697434,3535253d3db9edec |
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 72 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)