
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 152.44.110.240 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
152.44.110.240 has been assigned a threat score of 105/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
The following attack categories were identified:
Our monitoring infrastructure has identified 152.44.110.240, geolocated to Los Angeles, United States, operating on the network of Sprious LLC, as a source of suspicious network activity. During its 154-day observation window, we recorded 943 hostile requests from this IP — roughly 6.1 per day on average. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. With 102 flagged addresses, United States represents a significant presence in our threat database. At 105/100, this is an extremely high-risk address. All traffic should be considered hostile.
Request smuggling exploits differences in how front-end and back-end servers parse HTTP requests. This technique can bypass security controls, poison web caches, and hijack other users sessions by desynchronizing request boundaries.
Threat scoring combines multiple signals — request patterns, known signatures, IP reputation, geographic risk, and behavioral analysis — into a single actionable metric. Weighted scoring models allow tuning sensitivity to balance security with usability.