Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T186B238356A0401AB03BB99C1E6607F1FB6C7F30F85068511ABBDD19A1FD3CB6BB61462 |
|
CONTENT
ssdeep
|
192:6rVR95voygFAPFW0FuCFWF2FsFAMw27LFnNqmxs0Y2moLPJvESQfMS:KHvo9FuF7FuCFWF2FsFAM7s |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b33333338ccccccc |
|
VISUAL
aHash
|
c3c3ffffefe7ffff |
|
VISUAL
dHash
|
1e5d040c0c0c1c04 |
|
VISUAL
wHash
|
c3c3c3c3c3c3c3c3 |
|
VISUAL
colorHash
|
0700b000200 |
|
VISUAL
cropResistant
|
1e5d040c0c0c1c04,fefefffe7cfc80d0,bb5b7bfb3fcfcfc7 |
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 20 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)