IP Threat Report
172.213.25.235
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 184 | Medium-risk: admin panels, config files | +60 | |
| Burst: 44 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 144 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 180 | Medium-risk: admin panels, config files | +60 | |
| Burst: 50 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 180 req / 10s | Abnormally fast request rate — automated scanning | +35 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Network Provider
Recommendations
Actions taken & recommended
- IP 172.213.25.235 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 172.213.25.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🤖 User-Agent Anomaly Defense
IP 172.213.25.235 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
🌊 Traffic Flood Defense
IP 172.213.25.235 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Neighbors in 172.213.25.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
Blacklist Status (DNSBL)
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.
Threat Analysis
172.213.25.235 has been assigned a threat score of 245/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The following attack categories were identified:
📊 Threat Analysis
Network traffic from 172.213.25.235, located in Milan, Italy, operating on the network of Microsoft Corporation, has been classified as malicious by our automated threat scoring engine. During its 1-day observation window, we recorded 3 hostile requests from this IP — roughly 3 per day on average. Classified as a hosting IP, this address likely runs on a rented server or cloud instance. Attackers prefer datacenter IPs for their high bandwidth and disposable nature. The dual attack vectors of User-Agent Anomaly combined with Request Flooding indicate a coordinated assault rather than opportunistic scanning. Italy currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. A score of 245/100 places this address in the top tier of severity. Block and investigate any historical connections.
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.
Related Threats
Security Intelligence
💡 TLS Fingerprinting (JA3/JA4)
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.
💡 CAPTCHA and Bot Detection
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.