
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 177.65.250.95 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.
177.65.250.95 has been assigned a threat score of 103/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The address 177.65.250.95 originates from Itu, Brazil, operating on the network of Claro NXT Telecomunicacoes Ltda. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 26 days, this IP generated 2 malicious requests, averaging approximately 0.1 requests per day. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. Our records show 201 malicious IPs originating from Brazil, positioning it as a significant contributor to global threat activity. 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.
Vulnerability scanning is the automated process of probing web applications for known weaknesses. Attackers use tools like Nuclei, Nikto, and ZAP to test thousands of hosts per hour, looking for exposed configuration files, outdated software, and default credentials.
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.