
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| 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.
Block scanning from 49.36.101.3: 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.
49.36.101.3 has been assigned a threat score of 83/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
Our monitoring infrastructure has identified 49.36.101.3, geolocated to Mumbai, India, operating on the network of Reliance Jio Infocomm Limited, as a source of suspicious network activity. Our sensors captured 5 malicious requests from this address across a 2-day span, reflecting a sustained attack cadence of ~2.5 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. India currently accounts for 201 blocked IPs in our database, making it a significant source of malicious traffic. The score of 83/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
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.
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.