Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1C7821965A21422A84E4303DABE2253EFF207509DFB2217C5BAA9C12C75D96E9CC74DC6 |
|
CONTENT
ssdeep
|
192:QoKoB6CJ54t94yOSUMSNXMBCyFgITJQSdUrqzcqjHIku9cuGRmKbMpBXp7sfgg8B:QpoCbqMSN8WITJQSdUQDTsmMpBZ7eg/B |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
f52a55aa75aa5582 |
|
VISUAL
aHash
|
83c3e7c3c7e7e7e6 |
|
VISUAL
dHash
|
0f070f0f9e060f0e |
|
VISUAL
wHash
|
83c3e7c3c3c3c3c2 |
|
VISUAL
colorHash
|
07007000000 |
|
VISUAL
cropResistant
|
0f070f0f9e060f0e |
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 485 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)