
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| Danger strong hits: 4 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 3 | Medium-risk: admin panels, config files | +30 |
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 77.160.59.136: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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.
77.160.59.136 has been assigned a threat score of 155/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
Our monitoring infrastructure has identified 77.160.59.136, geolocated to Lelystad, Netherlands, operating on the network of KPN B.V., as a source of suspicious network activity. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. 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. The IP exhibits User-Agent manipulation, switching between different browser identities or sending empty headers. Our records show 121 malicious IPs originating from Netherlands, positioning it as a significant contributor to global threat activity. With a threat score of 155/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly 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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
When multiple IPs in a subnet show malicious behavior, subnet blocking efficiently neutralizes the threat. However, overly broad blocking risks impacting legitimate users. Analysis of subnet ownership and historical behavior guides appropriate blocking scope.