Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1EAD129E3C550403B456383E4F1722BA9E06AD10ECCDA0D6162EC5BEA1BE5DE5DA73D13 |
|
CONTENT
ssdeep
|
96:T4WXOe1W7WxW++wOmm8tbkE8Julpg0mwDibcFRZLBYHfwHSUXncd/:caOo+qr+wH94fNUXu |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
bc3f5390c74158c7 |
|
VISUAL
aHash
|
00818181ffdfffff |
|
VISUAL
dHash
|
1c1d2f17a4b292ea |
|
VISUAL
wHash
|
00010081ffdbdfff |
|
VISUAL
colorHash
|
07600030000 |
|
VISUAL
cropResistant
|
8000808080800080,1d1d2f47a6b292ea,03a48080c03f3737,71bee70918090111 |
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 20 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)