Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T19955157AEC0C5A09707675CDE3DC0D8FE995F357E72218E696C5CF31818A818B82A97C |
|
CONTENT
ssdeep
|
1536:0SrSXfBg8chNFteqTMW6wlD6xHWt0q7hNFteqTMW6wL96NVWt0FiJnP6Rr973iWn:fMfS8chNQDu0q7hNQlu0ghNQ5u0P0X6 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
e3731e4b5b107961 |
|
VISUAL
aHash
|
00e3c3e3f9fdffff |
|
VISUAL
dHash
|
390f8e9f434be013 |
|
VISUAL
wHash
|
00c3c1c1a1f9ffcb |
|
VISUAL
colorHash
|
06000600018 |
|
VISUAL
cropResistant
|
190f8e9f434be013,0418597939795804,9c9e998dad8b9a96 |
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 80 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)