Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T18673653D22C0563950C787F2F694AF29D29DCBD9DF27AD8BF2ACC247178AC458E51260 |
|
CONTENT
ssdeep
|
768:IWcFfRq3odbfXHwo4xBg28Wo+1v3PZkGEWba7TFeLcpMrH+K0PXd41eG2w:2fc3xZ1v3P1EWba721Ew |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
a9afd0b2c2e2dad0 |
|
VISUAL
aHash
|
ff0b0b2303030100 |
|
VISUAL
dHash
|
fbd7d3d3d7d79290 |
|
VISUAL
wHash
|
ff1b1b7b13030358 |
|
VISUAL
colorHash
|
07600008040 |
|
VISUAL
cropResistant
|
6bdfd3d3c3d7d7b3,2040838b73a36393,dfd3d3c3d7d39a90 |
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 25 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)