Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T149728370A4A2583F912B5AC1F4B07BAE60EAF30EDD5B4A14D3FC13AA5FD6C90E805115 |
|
CONTENT
ssdeep
|
384:9Yi9oqBiGRSHoGTLnSgSSbooJjev8YN/Q/5AdSTSSKYSQryh2zi1m2NW:ai9oqBbRY7fn/Ss5ZMtFCadcSDzQeEQU |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
be3050be81c9ef0d |
|
VISUAL
aHash
|
0000ffffffff0000 |
|
VISUAL
dHash
|
8c8c2800382b71d4 |
|
VISUAL
wHash
|
0000ffffffff0000 |
|
VISUAL
colorHash
|
1b000000180 |
|
VISUAL
cropResistant
|
80803008362c2f33,438c8c8c8c8c8c43,4b4aad2dadadaca4,2f2371b251550dd2 |
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 263 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)