
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Danger medium hits: 6 | Medium-risk: admin panels, config files | +60 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 |
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 199.34.84.250: 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-2026-33515 | NVD → |
| CVE-2023-46847 | NVD → |
| CVE-2023-49288 | NVD → |
| CVE-2021-31806 | NVD → |
| CVE-2024-25617 | NVD → |
| CVE-2023-49285 | NVD → |
| CVE-2021-31808 | NVD → |
| CVE-2023-49286 | NVD → |
| CVE-2021-28651 | NVD → |
| CVE-2021-28662 | NVD → |
| CVE-2023-50269 | NVD → |
| CVE-2021-31807 | NVD → |
| CVE-2024-45802 | NVD → |
| CVE-2021-46784 | NVD → |
| CVE-2023-46846 | NVD → |
| CVE-2023-46724 | NVD → |
| CVE-2022-41317 | NVD → |
| CVE-2021-28652 | NVD → |
| CVE-2025-54574 | NVD → |
| CVE-2021-33620 | NVD → |
| CVE-2021-28116 | NVD → |
| CVE-2022-41318 | NVD → |
| CVE-2025-62168 | NVD → |
| CVE-2023-5824 | NVD → |
| CVE-2026-32748 | NVD → |
🔴 Security scanning identified 30 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 This volume strongly suggests severely outdated software.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.
199.34.84.250 has been assigned a threat score of 105/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
The following attack categories were identified:
IP address 199.34.84.250 has been traced to Los Angeles, United States, operating on the network of Sprious LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 298 malicious requests from this address across a 41-day span, reflecting a sustained attack cadence of ~7.3 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. Our records show 200 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. 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.
Path traversal attacks attempt to access files outside the intended directory by manipulating file path references. Attackers use sequences like ../ to reach sensitive system files such as /etc/passwd or application configuration files.
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.