
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Foreign referer seen | Referer from unrelated external domain | +10 |
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 192.161.160.64: 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 |
|---|---|---|---|
| 80 | HTTP | Low | HTTP web server — standard web traffic |
| 123 | Unknown | Low | Service on port 123 |
| 161 | Unknown | Low | Service on port 161 |
| 21242 | Unknown | Low | Service on port 21242 |
| 52951 | Unknown | Low | Service on port 52951 |
| CVE ID | Link |
|---|---|
| CVE-2021-33620 | NVD → |
| CVE-2019-18676 | NVD → |
| CVE-2019-12526 | NVD → |
| CVE-2020-24606 | NVD → |
| CVE-2021-28116 | NVD → |
| CVE-2018-19132 | NVD → |
| CVE-2018-1000027 | NVD → |
| CVE-2015-5400 | NVD → |
| CVE-2025-59362 | NVD → |
| CVE-2020-15810 | NVD → |
| CVE-2019-12520 | NVD → |
| CVE-2020-8450 | NVD → |
| CVE-2020-15811 | NVD → |
| CVE-2024-37894 | NVD → |
| CVE-2023-49285 | NVD → |
| CVE-2021-31806 | NVD → |
| CVE-2019-18677 | NVD → |
| CVE-2025-54574 | NVD → |
| CVE-2019-12521 | NVD → |
| CVE-2019-13345 | NVD → |
| CVE-2018-1000024 | NVD → |
| CVE-2020-15049 | NVD → |
| CVE-2021-31807 | NVD → |
| CVE-2016-3947 | NVD → |
| CVE-2019-12524 | NVD → |
🔴 This host has 56 known CVEs associated with its exposed services. This volume strongly suggests severely outdated software. Review each CVE in the NVD database.
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.
192.161.160.64 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 following attack categories were identified:
The address 192.161.160.64 originates from Los Angeles, United States, operating on the network of HostPapa. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 1 days in our monitoring system, producing 1 flagged requests at a rate of ~1/day. This residential IP is likely a compromised consumer device. Home routers and IoT equipment with default credentials are prime targets for botnet operators. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. United States currently accounts for 200 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 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.
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.