Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1E6E14331D10728274223A6D5FAA1FB8922D3D70DCD0A5918AB5E01D92FE3FF5B493272 |
|
CONTENT
ssdeep
|
96:6r8XjfMx7dgudRoHGQwIOf/SAQsZU9JpsEEnyh2/qgHUrG6+c:61NRaW+vprT9B |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
a666668c3333999b |
|
VISUAL
aHash
|
e7e7e7e7e7e7e7e7 |
|
VISUAL
dHash
|
4d4d4d4d4d0f4d4d |
|
VISUAL
wHash
|
c3c3c3c3c3c3c3c3 |
|
VISUAL
colorHash
|
0e000000000 |
|
VISUAL
cropResistant
|
0000000000000000,8080808080808080,a288808c8c8880a2,cdccb20788aa3188 |
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 445 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)