
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 19 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 39 | Medium-risk: admin panels, config files | +60 | |
| Burst: 23 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 52 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 24 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 18 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 47 | Medium-risk: admin panels, config files | +60 | |
| Burst: 57 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 52 | Medium-risk: admin panels, config files | +60 | |
| Burst: 66 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 23 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 46 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 13 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 61 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 17 | High-risk paths: shells, RCE vectors, exploits | +100 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 52.138.0.148.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 111 | Unknown | Low | Service on port 111 |
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
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.
52.138.0.148 has been assigned a threat score of 230/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
IP address 52.138.0.148 has been traced to Toronto, Canada, operating on the network of Microsoft Corporation. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 72 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~72 requests per day. The IP is classified as hosting/datacenter infrastructure, commonly associated with rented servers used for automated attack campaigns, botnet command-and-control, or vulnerability scanning at scale. Rate-based attacks from this IP aim to overwhelm server resources through high-volume request flooding. With 103 flagged addresses, Canada represents a significant presence in our threat database. A score of 230/100 places this address in the top tier of severity. Block and investigate any historical connections.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
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.
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.