Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1E144F7D972C6F062139760B4A03F620FF3BB9E48680D9010E666E5C97D7985AD233F5E |
|
CONTENT
ssdeep
|
3072:ijrXfZmrwR7C/F1TpVMM25zDEd6DRWWXgapxl1eCDLMobqRaHsiU:EZmcBC/F1TpeMcvc6DRWWXgOn1eYLTS |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
af2f2fd0d0d0d0f0 |
|
VISUAL
aHash
|
0001019bffffffff |
|
VISUAL
dHash
|
6b634773949098e8 |
|
VISUAL
wHash
|
00000000ffffffff |
|
VISUAL
colorHash
|
06202008080 |
|
VISUAL
cropResistant
|
6b635373949298e8,0000219595959160,b2b633354d2f6ddb,8598888318ec6626,0181454545418101 |
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 1703 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)