Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T198728521610405A342B7AAC6E5717F1AB2D7F30FD64AD6446ABD81E80FC3CF8FA26571 |
|
CONTENT
ssdeep
|
192:6IVR/g3iui1iFjFlCFOpFzFDEM3vYdRDym9GHCyLtDVB9:DDJui1iFjF8FOpFzFDEM3/iK |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b2666666cecc8ccc |
|
VISUAL
aHash
|
c3c3c7ffffffffff |
|
VISUAL
dHash
|
4d4d0c064c120c5c |
|
VISUAL
wHash
|
03030303270b0303 |
|
VISUAL
colorHash
|
07018000200 |
|
VISUAL
cropResistant
|
4d4d0c064c120c5c,3fffff5f9f9f9f9f,fffbfae6ccb8fefe,94916909f3f6f6b6 |
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 323 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)