187.141.210.92 Threat Intelligence and Host Information

General

IP Address
187.141.210.92
IPv4 Address
Location
🇲🇽 Mexico
MX
Network
AS8151
Uninet S.A. de C.V.
Threat Score
45/100
Medium Risk
2026-012026-02attacker-ipautomatedAutomatedbruteforceBruteforce
Attack Intelligence
MITRE ATT&CK Techniques
T1110 - Brute Force
Open Ports Detected
111
Geographic Location
Country
Mexico
City
Unknown
Region
Unknown
Coordinates
19.4371, -99.0111
Network Information
ASN
AS8151
Organization
Uninet S.A. de C.V.
Network
AS8151 Uninet S.A. de C.V.
Attack Logs
Date Target Location Protocol Link
2026-04-05 Vultrmelbournetest SSH View Log

  • Country: Mexico
  • Network:
  • Noticed: 50 times
  • Protocols Attacked: portscan ssh
  • Countries Attacked: Australia
  • Passive DNS Results: sigaa.cobaev.edu.mx aspirantes.cobaev.edu.mx alumnos.cobaev.edu.mx apuntesacademicos.cobaev.edu.mx

CVEs Detected

CVE-2006-20001 CVE-2007-2768 CVE-2007-3205 CVE-2007-4559 CVE-2007-4723 CVE-2008-3844 CVE-2009-0796 CVE-2009-2299 CVE-2009-2940 CVE-2009-3720 CVE-2011-1176 CVE-2011-2688 CVE-2012-3526 CVE-2012-4001 CVE-2012-4360 CVE-2013-0340 CVE-2013-0941 CVE-2013-0942 CVE-2013-2220 CVE-2013-2765 CVE-2013-4352 CVE-2013-4365 CVE-2013-5704 CVE-2013-6438 CVE-2014-0098 CVE-2014-0117 CVE-2014-0118 CVE-2014-0226 CVE-2014-0231 CVE-2014-3523 CVE-2014-3581 CVE-2014-8109 CVE-2015-0228 CVE-2015-3183 CVE-2015-3184 CVE-2015-3185 CVE-2015-9253 CVE-2016-0736 CVE-2016-20012 CVE-2016-2161 CVE-2016-4975 CVE-2016-5387 CVE-2016-8612 CVE-2016-8743 CVE-2017-15710 CVE-2017-15715 CVE-2017-15906 CVE-2017-3167 CVE-2017-7272 CVE-2017-7679 CVE-2017-7963 CVE-2017-8923 CVE-2017-9788 CVE-2017-9798 CVE-2018-1283 CVE-2018-1301 CVE-2018-1302 CVE-2018-1303 CVE-2018-1312 CVE-2018-15473 CVE-2018-15919 CVE-2018-17199 CVE-2018-19395 CVE-2018-19396 CVE-2018-20406 CVE-2018-20685 CVE-2018-20852 CVE-2019-0217 CVE-2019-0220 CVE-2019-10092 CVE-2019-10098 CVE-2019-10160 CVE-2019-15903 CVE-2019-16056 CVE-2019-16935 CVE-2019-17567 CVE-2019-18348 CVE-2019-20907 CVE-2019-5010 CVE-2019-6109 CVE-2019-6110 CVE-2019-6111 CVE-2019-9636 CVE-2019-9637 CVE-2019-9638 CVE-2019-9639 CVE-2019-9641 CVE-2019-9674 CVE-2019-9740 CVE-2019-9947 CVE-2019-9948 CVE-2020-11579 CVE-2020-11985 CVE-2020-13938 CVE-2020-14145 CVE-2020-14422 CVE-2020-15523 CVE-2020-15778 CVE-2020-1927 CVE-2020-1934 CVE-2020-26116 CVE-2020-27619 CVE-2020-29396 CVE-2020-35452 CVE-2020-8315 CVE-2020-8492 CVE-2021-23336 CVE-2021-26690 CVE-2021-26691 CVE-2021-28861 CVE-2021-3177 CVE-2021-32785 CVE-2021-32786 CVE-2021-32791 CVE-2021-32792 CVE-2021-3426 CVE-2021-34798 CVE-2021-36368 CVE-2021-3733 CVE-2021-3737 CVE-2021-39275 CVE-2021-40438 CVE-2021-41617 CVE-2021-4189 CVE-2021-44790 CVE-2022-0391 CVE-2022-22719 CVE-2022-22720 CVE-2022-22721 CVE-2022-23943 CVE-2022-26377 CVE-2022-26488 CVE-2022-28330 CVE-2022-28614 CVE-2022-28615 CVE-2022-29404 CVE-2022-30556 CVE-2022-31628 CVE-2022-31629 CVE-2022-31813 CVE-2022-36760 CVE-2022-37436 CVE-2022-37454 CVE-2022-45061 CVE-2022-48560 CVE-2022-48564 CVE-2022-48565 CVE-2022-48566 CVE-2022-4900 CVE-2023-24329 CVE-2023-25690 CVE-2023-27043 CVE-2023-30861 CVE-2023-31122 CVE-2023-36632 CVE-2023-38408 CVE-2023-38709 CVE-2023-40217 CVE-2023-48795 CVE-2023-51385 CVE-2023-51767 CVE-2024-24795 CVE-2024-25117 CVE-2024-3566 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-2024-6232 CVE-2024-7592 CVE-2024-9287 CVE-2025-12084 CVE-2025-12781 CVE-2025-13836 CVE-2025-13837 CVE-2025-26465 CVE-2025-32728 CVE-2025-49812 CVE-2025-58098 CVE-2025-59775 CVE-2025-6075 CVE-2025-65082 CVE-2026-27205

Disclaimer
This page contains threat intelligence information for the IPv4 address 187.141.210.92 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.