Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1F1736334A12106778573CAD9E425BB5D30EBA30FD5C65A24DBBCC6B46FE6CE8B804C58 |
|
CONTENT
ssdeep
|
1536:lt6XOlrJLgbnOrWrO0LgbAXOOrtFSVZq46P2WqWlWpWxW8W6WpAeVZZnReWvWzWB:lLFLgbOSdLgbs76113 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
bb3649cdc469b684 |
|
VISUAL
aHash
|
b7818787fff9f9ff |
|
VISUAL
dHash
|
653b2f3fc42b33d3 |
|
VISUAL
wHash
|
00008385fff9b9fb |
|
VISUAL
colorHash
|
06000038040 |
|
VISUAL
cropResistant
|
0c65278e61796080,3b2b3f29642b33d2,0000186169691c20,01110d6168166008,334d2c65f4a16dc7 |
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 9 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)