Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T19BE265735401583F031367CAF426BB5DD093830FCAA698A4A2BD8B475FD6EE5C52DC2A |
|
CONTENT
ssdeep
|
768:U+wghtQIYM35c79jazwzJo4prjLj/JFk82AFXnaI8XCx0KRCtBIJ8xBIJ8pm3:U+wghtQIYM35c79jazwzJo4prjLj/JFT |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
93566d936c929356 |
|
VISUAL
aHash
|
00000e0e0e0f0f03 |
|
VISUAL
dHash
|
02d8dc58d8d85b03 |
|
VISUAL
wHash
|
830e7e2e2e2f2f03 |
|
VISUAL
colorHash
|
31000007000 |
|
VISUAL
cropResistant
|
32d2123002072b0d,02d8dc58d8d85b03,016928174d30b10d |
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 873 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)