Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1EEF1983581085D2EB117C6E4E6A2B629426ED347D35D8E5CF2E810E727CAD98C8273A8 |
|
CONTENT
ssdeep
|
96:O3sTYL/XN46YrhmcYUiwDk1xK55nDsjk4t29BknjukyZkhrZkUZkBLlM2pk0p75:OjL/yMcYy11sj3t29BejGorx6M2ppp75 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
8c7333cc26739966 |
|
VISUAL
aHash
|
3c18180018181807 |
|
VISUAL
dHash
|
68703212b0b2b20d |
|
VISUAL
wHash
|
fffe181810181f1f |
|
VISUAL
colorHash
|
38000200030 |
|
VISUAL
cropResistant
|
a200c050304100a2,a200807030f000a0,68703212b0b2b20d |
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 88 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)