
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| 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: 4 | Medium-risk: admin panels, config files | +40 | |
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 23.19.248.58 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
IP 23.19.248.58 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
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 |
| 4444 | Unknown | Low | Service on port 4444 |
| 7777 | Unknown | Low | Service on port 7777 |
| 36505 | Unknown | Low | Service on port 36505 |
| 44444 | Unknown | Low | Service on port 44444 |
| CVE ID | Link |
|---|---|
| CVE-2025-23419 | NVD → |
| CVE-2021-3618 | NVD → |
| CVE-2021-23017 | NVD → |
| CVE-2019-9516 | NVD → |
| CVE-2019-20372 | NVD → |
| CVE-2019-9511 | NVD → |
| CVE-2019-9513 | NVD → |
| CVE-2023-44487 | NVD → |
🔴 Security scanning identified 8 vulnerability entries on this host. Multiple vulnerabilities suggest gaps in patch management. 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.
23.19.248.58 has been assigned a threat score of 110/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The following attack categories were identified:
Our monitoring infrastructure has identified 23.19.248.58, geolocated to Seattle, United States, operating on the network of Leaseweb USA, Inc., as a source of suspicious network activity. Over a period of 27 days, this IP generated 48 malicious requests, averaging approximately 1.8 requests per day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The dual attack vectors of Path Enumeration combined with User-Agent Anomaly indicate a coordinated assault rather than opportunistic scanning. United States currently accounts for 148 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 110/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
Prototype pollution manipulates JavaScript object prototypes to inject properties that affect all objects in an application. This can lead to denial of service, property injection, and in some cases remote code execution in Node.js applications.
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.