IP Threat Report
171.7.238.194
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: 20 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 25 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 30 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 5 | Medium-risk: admin panels, config files | +50 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| 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 171.7.238.194 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 171.7.238.0/24
- 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 171.7.238.194: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🤖 Bot Detection
Address UA spoofing from 171.7.238.194: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Neighbors in 171.7.238.0/24
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
171.7.238.194 has been assigned a threat score of 130/100 (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
Threat intelligence analysis has linked 171.7.238.194 to malicious activity originating from Hat Yai, Thailand, operating on the network of Triple T Broadband Public Company Limited. The address has been under observation since its initial detection. Our sensors captured 283 malicious requests from this address across a 3-day span, reflecting a sustained attack cadence of ~94.3 requests per 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. Two attack patterns were identified (Path Enumeration and User-Agent Anomaly), suggesting a semi-automated campaign that targets multiple vulnerabilities. With 101 flagged addresses, Thailand represents a significant presence in our threat database. With a threat score of 130/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly 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.
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💡 Brute Force Attack Mechanics
Brute force attacks systematically try username and password combinations to gain unauthorized access. Modern attacks leverage credential databases from previous breaches, testing millions of combinations using distributed botnets across multiple IP addresses.
💡 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.