Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T139F240302154967B02D7C3D4D732ABAEB396814ACB730199E2F4C7599FC6EC5CC62B86 |
|
CONTENT
ssdeep
|
384:zLRWW5rRQFCdyK8XL9+MDyJqp379KksWhyUB5wf8m8dVm7CEuVN2jJqZ35lKC61:zLR17pW7sWhy03WsR61 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
9c9d9d62616565c6 |
|
VISUAL
aHash
|
1e3c3c0004000000 |
|
VISUAL
dHash
|
f070f012d4d4a200 |
|
VISUAL
wHash
|
7e7e3e04747e5a00 |
|
VISUAL
colorHash
|
380000001c8 |
|
VISUAL
cropResistant
|
5292ca4a4be8ce4f,f070f012d4d4a200 |
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 277738 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)