Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T104924030E529856E12EBD249AE446F8DC691B30FC749E6D6F4D0C29A2BC7DD1AC350CE |
|
CONTENT
ssdeep
|
192:h/sI4FK9/8u9NWpcWNEecph3DVcyCFDHUfi6wzUMVJOUceFsUk8kElkFkSgl7vRa:RxkDsiDUWiUk8kElkFkvJrp3c |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
e5656d611b1b1b13 |
|
VISUAL
aHash
|
00c3ffffe7ffffff |
|
VISUAL
dHash
|
96160e001686680a |
|
VISUAL
wHash
|
00c3c3e7c3ef00ef |
|
VISUAL
colorHash
|
06000010006 |
|
VISUAL
cropResistant
|
9686080a160c2808,cacacac84929c8c4,4947169696868696,01083032b2300801 |
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)