Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T1E482FF718238AE379067C1DAE6F66B2A31D1C20DCA4B0211C7FD93BD5BDACA5FD16084 |
|
CONTENT
ssdeep
|
192:IuWhZdHWj9M9he9Ds/DsdCxuCh//E9woEvtp8HsiOiAE6n:8WkhwYDsdWeCtDjjn |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
854a9f33591736c9 |
|
VISUAL
aHash
|
00f1f3004b077f3f |
|
VISUAL
dHash
|
e6e3e6a2922e96f6 |
|
VISUAL
wHash
|
00fcfb004b077f1f |
|
VISUAL
colorHash
|
06007000000 |
|
VISUAL
cropResistant
|
e6e3e6a2922e96f6 |
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 40 techniques to evade detection by security scanners and make reverse engineering more difficult.
Drainer scans for high-value tokens (USDT, USDC, SOL, memecoins) and prioritizes draining based on USD value. Low-value tokens are ignored to optimize transaction costs.
Pages with identical visual appearance (based on perceptual hash)