103.172.0.37 Threat Intelligence and Host Information
Feb 09, 2026
ipinfopage
General
IP Address
103.172.0.37
IPv4 Address
Location
🇮🇩 Ketintang, Indonesia
ID
Network
AS141126
PT Cubiespot Pilar Data Nusantara
Threat Score
15/100
Low Risk
Geographic Location
Coordinates
-6.9762, 112.3005
Network Information
Organization
PT Cubiespot Pilar Data Nusantara
Network
AS141126 PT Cubiespot Pilar Data Nusantara
WHOIS Information
inetnum
103.172.0.0 - 103.172.1.255
descr
Kecamatan Purworejo, Kota Pasuruan, Jawa Timur. 67127
mnt-routes
MAINT-ID-CUBIES
last-modified
2021-08-30T04:12:53Z
address
Pasuruan 67127, Indonesia
abuse-mailbox
support@cubies.id
person
Adam Whiter Utha Bramantya
- Country: Indonesia
- Network:
- Noticed: 1 times
- Protocols Attacked: Anonymous Proxy
CVEs Detected
CVE-2006-20001
CVE-2007-4723
CVE-2009-0796
CVE-2009-2299
CVE-2011-1176
CVE-2011-2688
CVE-2012-3526
CVE-2012-4001
CVE-2012-4360
CVE-2013-0941
CVE-2013-0942
CVE-2013-2765
CVE-2013-4365
CVE-2022-22719
CVE-2022-22720
CVE-2022-22721
CVE-2022-23943
CVE-2022-26377
CVE-2022-28330
CVE-2022-28614
CVE-2022-28615
CVE-2022-29404
CVE-2022-30556
CVE-2022-31813
CVE-2022-36760
CVE-2022-37436
CVE-2023-25690
CVE-2023-27522
CVE-2023-31122
CVE-2023-38709
CVE-2023-45802
CVE-2024-24795
CVE-2024-27316
CVE-2024-38472
CVE-2024-38473
CVE-2024-38474
CVE-2024-38475
CVE-2024-38476
CVE-2024-38477
CVE-2024-39573
CVE-2024-40898
CVE-2024-42516
CVE-2024-43204
CVE-2024-43394
CVE-2024-47252
CVE-2025-23048
CVE-2025-49630
CVE-2025-49812
CVE-2025-53020
CVE-2025-55753
CVE-2025-58098
CVE-2025-59775
CVE-2025-65082
CVE-2025-66200
Disclaimer
This page contains threat intelligence information for the IPv4 address 103.172.0.37 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.