
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | 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.
Block 103.171.246.64 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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.
103.171.246.64 has been assigned a threat score of 78/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.
Network traffic from 103.171.246.64, located in Kolkata, India, operating on the network of Gtpl Kcbpl Broadband PVT LTD, has been classified as malicious by our automated threat scoring engine. Our sensors captured 222 malicious requests from this address across a 5-day span, reflecting a sustained attack cadence of ~44.4 requests per day. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. India currently accounts for 103 blocked IPs in our database, making it a significant source of malicious traffic. A threat score of 78/100 places this IP in the high-risk category. Blocking at the firewall level is 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.
XXE vulnerabilities in XML parsers allow attackers to read local files, perform SSRF, and execute denial of service attacks. Many legacy applications and APIs remain vulnerable to XXE due to insecure default XML parser configurations.
Effective rate limiting must balance protection against abuse with allowing legitimate traffic bursts. Sliding window algorithms, token buckets, and adaptive thresholds based on client reputation provide layered defense against flooding attacks.