
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 6 | Medium-risk: admin panels, config files | +60 | |
| 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 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 98.158.235.234 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
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 |
| 60000 | Unknown | Low | Service on port 60000 |
| CVE ID | Link |
|---|---|
| CVE-2023-49288 | NVD → |
| CVE-2021-28116 | NVD → |
| CVE-2025-59362 | NVD → |
| CVE-2024-25111 | NVD → |
| CVE-2021-33620 | NVD → |
| CVE-2023-49286 | NVD → |
| CVE-2024-25617 | NVD → |
| CVE-2025-62168 | NVD → |
| CVE-2025-54574 | NVD → |
| CVE-2021-28651 | NVD → |
| CVE-2023-49285 | NVD → |
| CVE-2021-46784 | NVD → |
| CVE-2021-31806 | NVD → |
| CVE-2023-46847 | NVD → |
| CVE-2021-28662 | NVD → |
| CVE-2022-41317 | NVD → |
| CVE-2023-50269 | NVD → |
| CVE-2023-46724 | NVD → |
| CVE-2023-46728 | NVD → |
| CVE-2024-37894 | NVD → |
| CVE-2024-45802 | NVD → |
| CVE-2021-31807 | NVD → |
| CVE-2023-46846 | NVD → |
| CVE-2021-31808 | NVD → |
| CVE-2023-5824 | 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 This volume strongly suggests severely outdated software.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.
98.158.235.234 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 98.158.235.234 to malicious activity originating from Dallas, United States, operating on the network of Emeigh Investments LLC. The address has been under observation since its initial detection. Over a period of 11 days, this IP generated 2 malicious requests, averaging approximately 0.2 requests per day. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. United States currently accounts for 204 blocked IPs in our database, making it a significant source of malicious traffic. A score of 105/100 places this address in the top tier of severity. Block and investigate any historical connections.
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.