CVE-2022-31112
Published: 30 June 2022
Summary
CVE-2022-31112 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Parseplatform Parse-Server. Its CVSS base score is 8.2 (High).
Operationally, ranked in the top 30.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-6325
Vulnerability details
Parse Server is an open source backend that can be deployed to any infrastructure that can run Node.js. In affected versions parse Server LiveQuery does not remove protected fields in classes, passing them to the client. The LiveQueryController now removes…
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protected fields from the client response. Users are advised to upgrade. Users unable t upgrade should use `Parse.Cloud.afterLiveQueryEvent` to manually remove protected fields.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
The control's identification, isolation, alerting, and eradication steps directly limit the impact and exploitation window of unauthorized sensitive information exposure.
Proper media downgrading process prevents sensitive information from remaining on media that is then accessible to lower-classification recipients.
Policies requiring periodic review and deletion of inaccurate/outdated PII reduce the amount of sensitive information retained and therefore exposed.
Regular deletion of inaccurate or outdated PII directly reduces the volume of sensitive information retained that could be exposed.
De-identification directly prevents exposure of sensitive/PII data to unauthorized actors when datasets are released or shared.
Deleting information when no longer needed directly reduces the window during which sensitive data can be exposed to unauthorized actors.
Secure disposal techniques directly prevent sensitive data from becoming accessible to unauthorized actors after components leave organizational control.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.