
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Form spam: no_js_check | Spam/malware keywords in request content | +0 | |
| Imported from old blocklist | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Enable CAPTCHA on all public forms. Add honeypot fields. Rate-limit submissions to 3 per minute per IP. Deploy Akismet or CleanTalk.
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.
37.67.108.86 has been assigned a threat score of 70/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
The address 37.67.108.86 originates from Guyancourt, France, operating on the network of Societe Francaise Du Radiotelephone - SFR SA. It was identified through automated analysis of incoming network traffic across monitored endpoints. Our sensors captured 3 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~3 requests per day. This residential IP is likely a compromised consumer device. Home routers and IoT equipment with default credentials are prime targets for botnet operators. With 135 flagged addresses, France represents a significant presence in our threat database. The score of 70/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
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.
Signature-based detection matches known attack patterns but misses novel threats. Behavioral analysis identifies anomalies in request patterns, timing, and volume, catching zero-day attacks that signatures cannot recognize.