
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| DDoS / Flood | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Add 34.174.3.220 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
34.174.3.220 has been assigned a threat score of 235/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
IP address 34.174.3.220 has been traced to Dallas, United States, operating on the network of Google LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 1,095 malicious requests from this address across a 6-day span, reflecting a sustained attack cadence of ~182.5 requests per day. With 104 flagged addresses, United States represents a significant presence in our threat database. A score of 235/100 places this address in the top tier of severity. Block and investigate any historical connections.
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
GraphQL APIs introduce specific vulnerabilities including introspection information disclosure, query complexity attacks, batching abuse, and authorization bypass through nested queries. Depth limiting, cost analysis, and field-level authorization address these GraphQL-specific threats.