Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T15A034232A465B9374297B0D5A7326B4AA3E6E346CAE713D6B2F4C31C1FD6D51EC23201 |
|
CONTENT
ssdeep
|
384:niIcTXJy5xB7rSWjduWCDoH5/vwp9kecapv8848XE8SxsKsKVK4NhkKIyRKv7cVn:niDTJQxB7rtj5CNk9sfWA8BF |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
ca3516edba15e213 |
|
VISUAL
aHash
|
000064fffff90035 |
|
VISUAL
dHash
|
9c4dcccc2123eaed |
|
VISUAL
wHash
|
000074ffffff0035 |
|
VISUAL
colorHash
|
30000200088 |
|
VISUAL
cropResistant
|
9c4dcccc2123eaed |
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 94 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.