
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: crawler | Known bot/crawler User-Agent detected | +40 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Address UA spoofing from 57.141.0.46: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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.
57.141.0.46 has been assigned a threat score of 65/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
The following attack categories were identified:
IP address 57.141.0.46 has been traced to Ashburn, United States, operating on the network of Facebook, Inc.. Our threat detection systems have flagged this address based on observed malicious behavior patterns. The address has been active for 33 days in our monitoring system, producing 15 flagged requests at a rate of ~0.5/day. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. The IP exhibits User-Agent manipulation, switching between different browser identities or sending empty headers. With 199 flagged addresses, United States represents a significant presence in our threat database. At 65/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
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.
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.
False positives erode trust in security systems and waste analyst resources. Effective management requires feedback loops, allowlisting mechanisms, contextual analysis, and regular tuning of detection rules based on operational experience.