
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| Danger medium hits: 16 | 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 | |
| Imported from old blocklist | Behavioral anomaly detected by automated analysis | +0 | |
| Danger medium hits: 6 | Medium-risk: admin panels, config files | +60 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 47.129.249.45 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Block scanning from 47.129.249.45: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 8089 | Unknown | Low | Service on port 8089 |
| CVE ID | Link |
|---|---|
| CVE-2021-32791 | NVD → |
| CVE-2021-26691 | NVD → |
| CVE-2018-1302 | NVD → |
| CVE-2020-7061 | NVD → |
| CVE-2024-38472 | NVD → |
| CVE-2019-11046 | NVD → |
| CVE-2019-9020 | NVD → |
| CVE-2020-11579 | NVD → |
| CVE-2024-27316 | NVD → |
| CVE-2015-9253 | NVD → |
| CVE-2025-58098 | NVD → |
| CVE-2017-7668 | NVD → |
| CVE-2022-31628 | NVD → |
| CVE-2019-10082 | NVD → |
| CVE-2023-25690 | NVD → |
| CVE-2019-11042 | NVD → |
| CVE-2024-38475 | NVD → |
| CVE-2025-53020 | NVD → |
| CVE-2019-10092 | NVD → |
| CVE-2018-14883 | NVD → |
| CVE-2020-1927 | NVD → |
| CVE-2020-7067 | NVD → |
| CVE-2025-59775 | NVD → |
| CVE-2022-22720 | NVD → |
| CVE-2019-10081 | NVD → |
🔴 This host has 156 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 This volume strongly suggests severely outdated software.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.
47.129.249.45 has been assigned a threat score of 130/100 (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:
The address 47.129.249.45 originates from Singapore, Singapore, operating on the network of Amazon Technologies Inc. It was identified through automated analysis of incoming network traffic across monitored endpoints. Our sensors captured 5 malicious requests from this address across a 51-day span, reflecting a sustained attack cadence of ~0.1 requests per day. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. Two attack patterns were identified (User-Agent Anomaly and Path Enumeration), suggesting a semi-automated campaign that targets multiple vulnerabilities. Singapore currently accounts for 102 blocked IPs in our database, making it a significant source of malicious traffic. At 130/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
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.