
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 147.53.121.193 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 |
| CVE ID | Link |
|---|---|
| CVE-2024-37894 | NVD → |
| CVE-2023-46728 | NVD → |
| CVE-2022-41317 | NVD → |
| CVE-2023-49288 | NVD → |
| CVE-2021-31807 | NVD → |
| CVE-2021-33620 | NVD → |
| CVE-2021-46784 | NVD → |
| CVE-2022-41318 | NVD → |
| CVE-2023-46847 | NVD → |
| CVE-2025-54574 | NVD → |
| CVE-2021-31806 | NVD → |
| CVE-2024-25617 | NVD → |
| CVE-2021-28652 | NVD → |
| CVE-2024-25111 | NVD → |
| CVE-2021-28116 | NVD → |
| CVE-2023-46724 | NVD → |
| CVE-2023-50269 | NVD → |
| CVE-2021-31808 | NVD → |
| CVE-2023-5824 | NVD → |
| CVE-2025-59362 | NVD → |
| CVE-2023-49285 | NVD → |
| CVE-2024-45802 | NVD → |
| CVE-2025-62168 | NVD → |
| CVE-2021-28651 | NVD → |
| CVE-2023-46846 | NVD → |
🔴 This host has 27 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 This volume strongly suggests severely outdated software.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.
147.53.121.193 has been assigned a threat score of 105/100 (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:
Network traffic from 147.53.121.193, located in Dallas, United States, operating on the network of Blazing SEO, has been classified as malicious by our automated threat scoring engine. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 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. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. Our records show 201 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.
Credential stuffing uses stolen username-password pairs from data breaches to attempt logins across many websites. Since users frequently reuse passwords, these automated attacks achieve success rates of 0.1-2%, which translates to thousands of compromised accounts from millions of attempts.
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.