Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T16412BE342401196B22731BC97DE2A7CE60D7E30FCA5B96115AEC438A5FD3DA4A871B25 |
|
CONTENT
ssdeep
|
96:CD7C2PpYiPpJ5dPNkrFzZ2sfMWoEdXPpDTCZWjNTy+PpeZuk117zM/xCt4uZdq7Z:CDegnU5oC/oAJZO0c8ezx9ysSPH4dmR |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
a68c333399997723 |
|
VISUAL
aHash
|
e7e7e7dbffe7e7e7 |
|
VISUAL
dHash
|
4d4c0cb3314c4c4d |
|
VISUAL
wHash
|
2424343c27270707 |
|
VISUAL
colorHash
|
0e000007000 |
|
VISUAL
cropResistant
|
a288104d4d4ca8a2,4d4c0cb3314c4c4d |
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 83 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)