
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 193.106.249.102 is flooding forms with spam. Implement time-based tokens and block IPs submitting more than 5 forms per hour.
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.
193.106.249.102 has been assigned a threat score of 70/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.
The address 193.106.249.102 originates from Frankfurt am Main, Germany, operating on the network of GTHost. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. 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. A threat score of 70/100 places this IP in the high-risk category. Blocking at the firewall level is 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.
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.
IP geolocation databases provide approximate locations with varying accuracy. City-level geolocation is typically 50-80% accurate, while country-level exceeds 95%. VPNs, proxies, and mobile networks further reduce reliability, making geolocation a useful but imperfect intelligence signal.