Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T12522A678C4D1A43B5267C5E4A9F1BBADE05BD307C7860F90D2F882E90EC2E94E933559 |
|
CONTENT
ssdeep
|
192:Yi73xBMNi3pWs6Bh0fjftULKJEhdll8co3bZDP/QEeOP8p7BAAeUDtKEa8GsRrPw:YQBBMNi3Ms6Bh0fjftUEEhdll8co3bZD |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
935c4c5d4c4673b3 |
|
VISUAL
aHash
|
0000efffffefffff |
|
VISUAL
dHash
|
f0d80c4c1c5c0c0c |
|
VISUAL
wHash
|
0000c7e7e7c7c7c7 |
|
VISUAL
colorHash
|
07007000080 |
|
VISUAL
cropResistant
|
f0d80c4c1c5c0c0c,ffffffbfbf9fffff,0000000000000000 |
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 5 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)