
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Form spam: no_js_check | Spam/malware keywords in request content | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 45.86.202.160 is flooding forms with spam. Implement time-based tokens and block IPs submitting more than 5 forms per hour.
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.
45.86.202.160 has been assigned a threat score of 70/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
IP address 45.86.202.160 has been traced to Frankfurt am Main, Germany, operating on the network of F.N.S. HOLDINGS LIMITED. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 requests per day. Classified as a VPN or proxy server, this IP serves as an anonymization layer. While VPNs have legitimate uses, this address has been observed routing clearly malicious traffic. Germany currently accounts for 135 blocked IPs in our database, making it a significant source of malicious traffic. A threat score of 70/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
SQL injection remains one of the most common web attack vectors. Attackers inject malicious SQL code through input fields to extract database contents, modify data, or gain administrative access. Automated scanners test for SQLi vulnerabilities at massive scale.
Analyzing network flows (NetFlow, sFlow, IPFIX) provides visibility into traffic patterns without inspecting packet contents. Flow data reveals scanning activity, data exfiltration, lateral movement, and command-and-control channels at scale.