
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA changed | Multiple User-Agents — bot rotation technique | +25 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 172.59.27.6 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Block scanning from 172.59.27.6: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
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.
172.59.27.6 has been assigned a threat score of 60/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
The following attack categories were identified:
Network traffic from 172.59.27.6, located in Cleveland, United States, operating on the network of T-Mobile USA, Inc., has been classified as malicious by our automated threat scoring engine. The address has been active for 1 days in our monitoring system, producing 1 flagged requests at a rate of ~1/day. The address belongs to a mobile carrier network. The sustained pattern of malicious requests indicates either a compromised device or deliberate abuse. The dual attack vectors of User-Agent Anomaly combined with Path Enumeration indicate a coordinated assault rather than opportunistic scanning. With 107 flagged addresses, United States represents a significant presence in our threat database. At 60/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
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.
SQL injection remains one of the most common web attack vectors. Attackers inject malicious SQL code through input fields to extract database contents, modify data, or gain administrative access. Automated scanners test for SQLi vulnerabilities at massive scale.