Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1BE521DB9B31011E09E0387DEFA2232FAE113817EDB525ADCD3644618B295DFD8965EC2 |
|
CONTENT
ssdeep
|
192:QoqoBmJ5/9IdF8JYbswv9vMt3Mu9cuGRmKbMpBXp7sfgg8gk:QRoAJJY5FvPsmMpBZ7eg/B |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
bf80752ac5ead4a2 |
|
VISUAL
aHash
|
9f87ffbf81818181 |
|
VISUAL
dHash
|
577d796971796173 |
|
VISUAL
wHash
|
d38fffbd81818181 |
|
VISUAL
colorHash
|
06006000000 |
|
VISUAL
cropResistant
|
577d796971796173,58dbaaa69498f4a6,a59c5a7575bab260,b5fdf979e5f9e1e7 |
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 490 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)