Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1B3B32431A0A191BE41AFA1C3A1387F05A2E7E347DA1947D267FB03848FD7D61FA13625 |
|
CONTENT
ssdeep
|
768:fTCnq3W87kBkNY4pKDn8PS8/319jfIhYkVIuxhqGrkEmYie0rmFJ+gHCY1cF:f7p4DpkEsrmE |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
e4646464939b9b9b |
|
VISUAL
aHash
|
c3c3cfffc3e7ffff |
|
VISUAL
dHash
|
0686961896161a1a |
|
VISUAL
wHash
|
81c3cbc3c3c3c3cb |
|
VISUAL
colorHash
|
07200080006 |
|
VISUAL
cropResistant
|
0686961896161a1a,0466070d0c0c1c1e,001004b2b2300800 |
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 5 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)