CVE-2022-31162
Published: 22 July 2022
Summary
CVE-2022-31162 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Slack Morphism Project Slack Morphism. Its CVSS base score is 7.5 (High).
Operationally, ranked in the top 39.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-6305
Vulnerability details
Slack Morphism is an async client library for Rust. Prior to 0.41.0, it was possible for Slack OAuth client information to leak in application debug logs. Stricter and more secure debug formatting was introduced in v0.41.0 for OAuth secret types…
more
to reduce the possibility of printing sensitive information in application logs. As a workaround, do not print/output requests and responses for OAuth and client configurations in logs.
- 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Policies requiring periodic review and deletion of inaccurate/outdated PII reduce the amount of sensitive information retained and therefore exposed.
Regular deletion of inaccurate or outdated PII directly reduces the volume of sensitive information retained that could be exposed.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.