Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T16B4374326444243B532757C9B022771EF193A30ECA8748A9F3FD8B934FE3D95991986B |
|
CONTENT
ssdeep
|
1536:469A62FvhtQIq65350PcQ2kw2edD81quTa23M7Kb0VOU3j9gOsN75Zdl49h2SURB:zwvtmZTa23h7JVFeWyolfwHD1g4cXKTg |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
a152af2d52adad52 |
|
VISUAL
aHash
|
000444404003033f |
|
VISUAL
dHash
|
420c8c8c8c8f47cf |
|
VISUAL
wHash
|
e70666766407037f |
|
VISUAL
colorHash
|
38040006000 |
|
VISUAL
cropResistant
|
68ede7dbd5d966ea,420c8c8c8c8f47cf,01d978270d30710d |
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 813 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)