Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T116522ABDB22422F59E0343DAF96223BAF21341BE9A6256DCD35543187399DFD8821DC2 |
|
CONTENT
ssdeep
|
192:QoYoBYJ5J9kg+1UMYxIqIvKysku9cuGRmKbMpBXp7sfgg8gk:Qzog9+TYtIvKyHsmMpBZ7eg/B |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
efe89292c03a1d6e |
|
VISUAL
aHash
|
fff910f0f01011ff |
|
VISUAL
dHash
|
4d73636343737162 |
|
VISUAL
wHash
|
fffb10f0f00000ff |
|
VISUAL
colorHash
|
07038000000 |
|
VISUAL
cropResistant
|
4d73636343737162,47c7c7c3e3630786,fffddbfaf5ffdfd9,80c0c0c0800040c4 |
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 482 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)