161.97.136.67 Threat Intelligence and Host Information

General

IP Address
161.97.136.67
IPv4 Address
Location
🇩🇪 Düsseldorf, Germany
DE
Network
AS51167
Contabo GmbH
Threat Score
39/100
Medium Risk
bruteforceexploitportscanscannerstelnettpotvulnerability-exploitationvultr
Attack Intelligence
Open Ports Detected
110
Geographic Location
Country
Germany
City
Düsseldorf
Region
North Rhine-Westphalia
Coordinates
51.1878, 6.8607
Network Information
ASN
AS51167
Organization
Contabo GmbH
Network
AS51167 Contabo GmbH
WHOIS Information
NetRange
161.97.64.0 - 161.97.189.255
CIDR
161.97.160.0/20, 161.97.128.0/19, 161.97.176.0/21, 161.97.188.0/23, 161.97.184.0/22, 161.97.64.0/18
NetName
RIPE
NetHandle
NET-161-97-64-0-1
Parent
NET161 (NET-161-0-0-0-0)
NetType
Early Registrations, Transferred to RIPE NCC
OriginAS
Organization
RIPE Network Coordination Centre (RIPE)
RegDate
Updated
2013-07-29
Ref
https://rdap.arin.net/registry/entity/RIPE
OrgName
RIPE Network Coordination Centre
OrgId
RIPE
Address
P.O. Box 10096
City
Amsterdam
StateProv
PostalCode
1001EB
Country
NL
OrgAbuseHandle
ABUSE3850-ARIN
OrgAbuseName
Abuse Contact
OrgAbusePhone
+31205354444
OrgAbuseEmail
abuse@ripe.net
OrgAbuseRef
https://rdap.arin.net/registry/entity/ABUSE3850-ARIN
OrgTechHandle
RNO29-ARIN
Attack Logs
Date Target Location Protocol Link
2026-05-04 Vultrparis TELNET View Log

  • Country: Germany
  • Network:
  • Noticed: 3 times
  • Protocols Attacked: portscan telnet
  • Passive DNS Results: paashe.xyz ns2.uisoit.com shironamnews.online dbvps.tricks4pk.com iamtoky.com

CVEs Detected

CVE-2026-40684 CVE-2026-40685 CVE-2026-40686 CVE-2026-40687

Disclaimer
This page contains threat intelligence information for the IPv4 address 161.97.136.67 and was generated either as a result of observed malicious activity or as an information gathering exercise to assist with enrichment of security events and context. All information is gathered passively through aggregation of public sources, or observations through activity upon honeynets. The host score is calculated through a series of statistically weighted values and machine learning which takes into account metadata such as host information, frequency, volume and global distribution of malicious activity, association with other known malicious hosts or networks, proxying or anonymising behaviour such as with tor exit nodes, residential proxies or VPN services, and many other attributes. These values are historical and indicative only - and should not be taken to be an accurate representation of the users, businesses or networks in which they reside.