Login-Server vulnerabilitiesRanked by how likely they are to be used.
Login-Server is made by Pivotal Software. We track 2 published vulnerabilities in it, across 0 versions. None of them is on the list of flaws known to be exploited today. If you run Login-Server, keep it on a version the vendor still supports.
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Which Login-Server vulnerabilities are being exploited?
This is the list that matters most: flaws criminals are using now.
None of Login-Server's published vulnerabilities is on the list of flaws known to be exploited today. We keep watching that list.
Which Login-Server flaws are most likely to be used?
Ranked by the latest exploit-likelihood forecast: the chance each flaw is used in an attack in the next 30 days. A high severity score on its own doesn't tell you that. Flaws shared by many other makers' products are left out.
- 1.2%CVE-2016-3084High
The UAA reset password flow in Cloud Foundry release v236 and earlier versions, UAA release v3.3.0 and earlier versions, all versions of Login-server, UAA release v10 and earlier versions and Pivotal Elastic Runtime...
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1 new flaw affects software Acme tracks
The UAA reset password flow in Cloud Foundry release v236 and earlier versions, UAA release v3.3.0 and earlier versions, all versions of Login-server, UAA release v10 and earlier versions and Pivotal Elastic Runtime versions prior to 1.7.2 is vulnerable to a brute force attack due to multiple active codes at a given time. This vulnerability is applicable only when using the UAA internal user store for authentication. Deployments enabled for integration via SAML or LDAP are not affected.
- Matched to
- Login-Server on Acme's software list
- What to do
- Plan in the vendor’s update
Sent to the alert recipients Acme chose. Also in the app and to Acme's webhook.
We track 2 published vulnerabilities listed under the name Login-Server, across 0 versions.
None of Login-Server's vulnerabilities is on the list of flaws known to be exploited today.
Create a Precursor Intelligence account and track Login-Server. We match it against every published flaw, give each one a risk score from 0 to 100 and keep watching, so you hear about new ones as soon as we know, by email, in the app or by webhook.