
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| POST requests present | Behavioral anomaly detected by automated analysis | +8 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Add 112.207.134.203 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
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.
112.207.134.203 has been assigned a threat score of 103/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
IP address 112.207.134.203 has been traced to Makati City, Philippines, operating on the network of Philippine Long Distance Telephone Co.. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. Our records show 157 malicious IPs originating from Philippines, positioning it as a significant contributor to global threat activity. A score of 103/100 places this address in the top tier of severity. Block and investigate any historical connections.
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.
Credential stuffing uses stolen username-password pairs from data breaches to attempt logins across many websites. Since users frequently reuse passwords, these automated attacks achieve success rates of 0.1-2%, which translates to thousands of compromised accounts from millions of attempts.
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.