IP Threat Report
216.73.161.151
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| UA bot: Go-http-client | Known bot/crawler User-Agent detected | +40 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 |
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 216.73.161.151 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 216.73.161.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🔎 Path Enumeration Protection
Block scanning from 216.73.161.151: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🤖 User-Agent Anomaly Defense
IP 216.73.161.151 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Neighbors in 216.73.161.0/24
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
216.73.161.151 has been assigned a threat score of 100/100 (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:
📊 Threat Analysis
Our monitoring infrastructure has identified 216.73.161.151, geolocated to New York, United States, operating on the network of Bandito Networks, as a source of suspicious network activity. Our sensors captured 192 malicious requests from this address across a 9-day span, reflecting a sustained attack cadence of ~21.3 requests per day. Classified as a VPN or proxy server, this IP serves as an anonymization layer. While VPNs have legitimate uses, this address has been observed routing clearly malicious traffic. The dual attack vectors of Path Enumeration combined with User-Agent Anomaly indicate a coordinated assault rather than opportunistic scanning. Our records show 151 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. At 100/100, this is an extremely high-risk address. All traffic should be considered hostile.
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
Related Threats
🇺🇸 Top threats from United States
104.28.246.115 (350)104.28.214.122 (350)103.168.66.237 (340)34.187.158.4 (340)34.186.187.208 (340)View all →Security Intelligence
💡 Vulnerability Scanning Explained
Vulnerability scanning is the automated process of probing web applications for known weaknesses. Attackers use tools like Nuclei, Nikto, and ZAP to test thousands of hosts per hour, looking for exposed configuration files, outdated software, and default credentials.
💡 Web Application Firewall Strategies
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.