
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 158.173.152.143 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.
158.173.152.143 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.
Threat intelligence analysis has linked 158.173.152.143 to malicious activity originating from Zurich, Switzerland, operating on the network of Datacamp Limited. The address has been under observation since its initial detection. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 requests per day. The address operates as a VPN/proxy exit node. Attackers route traffic through anonymizing services to obscure their real location and evade IP-based security controls. Switzerland currently accounts for 113 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.
GraphQL APIs introduce specific vulnerabilities including introspection information disclosure, query complexity attacks, batching abuse, and authorization bypass through nested queries. Depth limiting, cost analysis, and field-level authorization address these GraphQL-specific threats.