
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Burst: 5 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 6 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Imported from old blocklist | Behavioral anomaly detected by automated analysis | +0 | |
| Burst: 7 req / 2s | Abnormally fast request rate — automated scanning | +35 |
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 67.99.173.2: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
IP 67.99.173.2 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
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 |
|---|---|---|---|
| 22 | SSH | Low | Secure Shell — common brute force target for remote access |
| CVE ID | Link |
|---|---|
| CVE-2021-36368 | NVD → |
| CVE-2025-26465 | NVD → |
| CVE-2024-6387 | NVD → |
| CVE-2025-32728 | NVD → |
| CVE-2021-41617 | NVD → |
| CVE-2023-38408 | NVD → |
| CVE-2023-51767 | NVD → |
| CVE-2008-3844 | NVD → |
| CVE-2023-51385 | NVD → |
| CVE-2016-20012 | NVD → |
| CVE-2007-2768 | NVD → |
| CVE-2023-48795 | NVD → |
🔴 Security scanning identified 12 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 This volume strongly suggests severely outdated software.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.
67.99.173.2 has been assigned a threat score of 170/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The following attack categories were identified:
Threat intelligence analysis has linked 67.99.173.2 to malicious activity originating from Clarence Center, United States, operating on the network of Level 3 Communications. The address has been under observation since its initial detection. Our sensors captured 16 malicious requests from this address across a 18-day span, reflecting a sustained attack cadence of ~0.9 requests per 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. The dual attack vectors of Path Enumeration combined with Request Flooding indicate a coordinated assault rather than opportunistic scanning. With 102 flagged addresses, United States represents a significant presence in our threat database. A score of 170/100 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.
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.