Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T156C3417012282A77671B4AB1F0A1FFD9A0DAF349D61BC82563FC52676FCECD08A91750 |
|
CONTENT
ssdeep
|
3072:vRncLF/BR/X3/jW/cn/J3/ax//L/YX/Uz/Y7mCrSIT222LSR0i6hb:wmCrSI0 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
b6ca58b0cbb0b4ce |
|
VISUAL
aHash
|
fd00372737070700 |
|
VISUAL
dHash
|
7941ec4e6eae2e2c |
|
VISUAL
wHash
|
ff30773737070700 |
|
VISUAL
colorHash
|
06400008003 |
|
VISUAL
cropResistant
|
c03e363535262ed6,a1a9a9a9a9a9a9a9,3b3d2923c4d4e8c8,2ba9d4697880696b,58d9998bc1e030f8,7941ec4e6eae2e2c,3249697171694c10 |
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 143 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)