Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T16AA2A73813442B3E562747D4F6A8B33981A9D39AD22B895DF2BC127223C7D94DD732D8 |
|
CONTENT
ssdeep
|
384:r9YSYr3RfqfY3IED4qq8MyYQhVW52RfzzSN:5YSYrRfqfYjXYQhVW52tzzSN |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
c50db972a6c1a76a |
|
VISUAL
aHash
|
ff10383860c0c0ff |
|
VISUAL
dHash
|
1b7070f08d8c8882 |
|
VISUAL
wHash
|
ff18383cc0e0c0ff |
|
VISUAL
colorHash
|
1bc00000000 |
|
VISUAL
cropResistant
|
0002612b2b810028,fce404b0bcbce063,ee5bf292e432f94d,8488988888920000,f06072e48dcc8888 |
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 14 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)