Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1FEF220B1D044A53B019BD1D4E33CBB1A73E79286CD6A42A563FD835C4FCEE92E411D1A |
|
CONTENT
ssdeep
|
192:PHK+z2rH7sh7avOamvCDtw84HwpsUJ1Lj7bNtZokvfonWydv/W8q5tbOjdFm3mvc:PHB6rbsQLj7xttvfoWVSdkN |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b1646464339b9b9b |
|
VISUAL
aHash
|
c3c3ffefc3ffffff |
|
VISUAL
dHash
|
0686121e961a1a1a |
|
VISUAL
wHash
|
81c3cbc3c3c3c3cb |
|
VISUAL
colorHash
|
070000c0003 |
|
VISUAL
cropResistant
|
0686121e961a1a1a,4f0d274747072323,4108047031140041 |
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)