
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 138.128.140.79 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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.
138.128.140.79 has been assigned a threat score of 103/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The address 138.128.140.79 originates from Elk Grove Village, United States, operating on the network of i3D.net B.V. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. United States currently accounts for 106 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 103/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
SSRF attacks trick servers into making requests to internal resources that should not be publicly accessible. This can expose cloud metadata endpoints, internal APIs, and private network services, potentially leading to full infrastructure compromise.
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.