
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| Burst: 6 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| 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 5.252.153.60 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 5.252.153.60.
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 |
| 80 | HTTP | Low | HTTP web server — standard web traffic |
| 3389 | RDP | High | Remote Desktop Protocol — primary target for ransomware attacks |
⚠️ 1 high-risk port detected on 5.252.153.60. Exposed RDP (3389) is the #1 entry point for ransomware attacks. These services should not be publicly accessible without strict firewall rules.
| CVE ID | Link |
|---|---|
| CVE-2025-23419 | NVD → |
| CVE-2023-44487 | NVD → |
| CVE-2021-3618 | NVD → |
| CVE-2021-23017 | NVD → |
🔴 This host has 4 known CVEs associated with its exposed services. Multiple vulnerabilities suggest gaps in patch management. 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.
5.252.153.60 has been assigned a threat score of 210/100 (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:
5.252.153.60 is registered in Amsterdam, Netherlands, operating on the network of Partner Hosting LTD. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 requests per day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The dual attack vectors of User-Agent Anomaly combined with Request Flooding indicate a coordinated assault rather than opportunistic scanning. Netherlands currently accounts for 103 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 210/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
TLS fingerprinting creates unique identifiers based on how clients negotiate encrypted connections. The JA3 and JA4 methods generate hashes from TLS ClientHello parameters, enabling identification of specific tools and malware regardless of IP address changes.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.