Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T16C34A476201D0F271AF6C3EFA4A9BF50609F2B1CDC1A2AB5F1BCE23897D5E41A613454 |
|
CONTENT
ssdeep
|
1536:6WFhuuWzMOVq+S7k8Rl3qqIq/IRxzDzXSmNIlbtufynHuMM5ouwkXwvbhWKo2h2I:3FROqcT3SdbO3XNiMuZgNnS32Of3 |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
c3873c793196c16d |
|
VISUAL
aHash
|
00007e7e60608000 |
|
VISUAL
dHash
|
9f63c4cccbdd2694 |
|
VISUAL
wHash
|
43037f7f7361e0c0 |
|
VISUAL
colorHash
|
3800b008000 |
|
VISUAL
cropResistant
|
6a684a97a5a53434,9f63c4cccbdd2694 |
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 51 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.