
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 3 | Medium-risk: admin panels, config files | +30 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| Danger medium hits: 7 | Medium-risk: admin panels, config files | +60 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block scanning from 130.254.112.18: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
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 |
|---|---|---|---|
| 4444 | Unknown | Low | Service on port 4444 |
| 8000 | Unknown | Low | Service on port 8000 |
| CVE ID | Link |
|---|---|
| CVE-2021-33620 | NVD → |
| CVE-2024-25111 | NVD → |
| CVE-2021-28116 | NVD → |
| CVE-2025-62168 | NVD → |
| CVE-2021-31807 | NVD → |
| CVE-2023-5824 | NVD → |
| CVE-2023-49285 | NVD → |
| CVE-2025-54574 | NVD → |
| CVE-2021-31806 | NVD → |
| CVE-2021-46784 | NVD → |
| CVE-2021-28652 | NVD → |
| CVE-2022-41317 | NVD → |
| CVE-2023-46847 | NVD → |
| CVE-2023-49288 | NVD → |
| CVE-2021-28662 | NVD → |
| CVE-2023-46724 | NVD → |
| CVE-2023-50269 | NVD → |
| CVE-2024-45802 | NVD → |
| CVE-2024-37894 | NVD → |
| CVE-2024-25617 | NVD → |
| CVE-2022-41318 | NVD → |
| CVE-2023-49286 | NVD → |
| CVE-2021-31808 | NVD → |
| CVE-2023-46846 | NVD → |
| CVE-2025-59362 | NVD → |
🔴 Security scanning identified 27 vulnerability entries on this host. This volume strongly suggests severely outdated software. Consult NVD advisories for details.
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.
130.254.112.18 has been assigned a threat score of 105/100 (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:
Threat intelligence analysis has linked 130.254.112.18 to malicious activity originating from Los Angeles, United States, operating on the network of Blazing SEO. The address has been under observation since its initial detection. The address has been active for 22 days in our monitoring system, producing 6 flagged requests at a rate of ~0.3/day. 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. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. United States currently accounts for 206 blocked IPs in our database, making it a significant source of malicious traffic. At 105/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
XXE vulnerabilities in XML parsers allow attackers to read local files, perform SSRF, and execute denial of service attacks. Many legacy applications and APIs remain vulnerable to XXE due to insecure default XML parser configurations.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.