
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| POST requests present | Behavioral anomaly detected by automated analysis | +8 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block 37.8.161.63 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
37.8.161.63 has been assigned a threat score of 103/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
Our monitoring infrastructure has identified 37.8.161.63, geolocated to Paris, France, operating on the network of Free Mobile SAS, as a source of suspicious network activity. The address has been active for 1 days in our monitoring system, producing 1 flagged requests at a rate of ~1/day. This is a mobile network IP. While mobile addresses are typically shared via CGNAT, persistent malicious activity from this specific address suggests automated abuse. France currently accounts for 102 blocked IPs in our database, making it a significant source of malicious traffic. At 103/100, this is an extremely high-risk address. All traffic should be considered hostile.
WordPress sites face constant automated attacks targeting xmlrpc.php for brute force amplification, wp-login.php for credential theft, and vulnerable plugins for remote code execution. Over 90% of CMS-based attacks specifically target WordPress installations.
Insider threats — whether malicious or negligent — account for a significant percentage of data breaches. Behavioral analytics detecting unusual access patterns, data downloads, and privilege escalation help identify insider risks before damage occurs.