
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| 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.
Block scanning from 152.89.129.222: 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 |
|---|---|---|---|
| 80 | HTTP | Low | HTTP web server — standard web traffic |
| 3128 | Unknown | Low | Service on port 3128 |
| 8000 | Unknown | Low | Service on port 8000 |
| 8080 | HTTP-Alt | Low | HTTP alternative port — often used for admin panels or proxies |
| 8800 | Unknown | Low | Service on port 8800 |
| CVE ID | Link |
|---|---|
| CVE-2016-4553 | NVD → |
| CVE-2016-4554 | NVD → |
| CVE-2016-2390 | NVD → |
| CVE-2025-62168 | NVD → |
| CVE-2020-8517 | NVD → |
| CVE-2020-14058 | NVD → |
| CVE-2021-31807 | NVD → |
| CVE-2019-18679 | NVD → |
| CVE-2019-12523 | NVD → |
| CVE-2021-28651 | NVD → |
| CVE-2019-12524 | NVD → |
| CVE-2015-5400 | NVD → |
| CVE-2019-18860 | NVD → |
| CVE-2020-15810 | NVD → |
| CVE-2019-12521 | NVD → |
| CVE-2019-18678 | NVD → |
| CVE-2016-3947 | NVD → |
| CVE-2020-8450 | NVD → |
| CVE-2022-41318 | NVD → |
| CVE-2023-5824 | NVD → |
| CVE-2020-24606 | NVD → |
| CVE-2019-12522 | NVD → |
| CVE-2019-18677 | NVD → |
| CVE-2019-12519 | NVD → |
| CVE-2026-32748 | NVD → |
🔴 This host has 59 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 This volume strongly suggests severely outdated software.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.
152.89.129.222 has been assigned a threat score of 85/100 (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:
Network traffic from 152.89.129.222, located in London, United Kingdom, operating on the network of XT GLOBAL NETWORKS LTD., has been classified as malicious by our automated threat scoring engine. The address has been active for 1 days in our monitoring system, producing 1 flagged requests at a rate of ~1/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 Kingdom currently accounts for 30 blocked IPs in our database, making it a notable source of malicious traffic. A threat score of 85/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
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.
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.
CAPTCHAs remain a primary bot defense but face increasing bypass rates from AI-powered solvers. Modern alternatives include invisible behavioral analysis, proof-of-work challenges, and device fingerprinting that detect bots without impacting user experience.