CVE-2025-32958
Published: 21 April 2025
Summary
CVE-2025-32958 is a critical-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability. Its CVSS base score is 9.8 (Critical).
Operationally, ranked at the 31.7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-14690
Vulnerability details
Adept is a language for general purpose programming. Prior to commit a1a41b7, the remoteBuild.yml workflow file uses actions/upload-artifact@v4 to upload the mac-standalone artifact. This artifact is a zip of the current directory, which includes the automatically generated .git/config file containing…
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the run's GITHUB_TOKEN. Seeing as the artifact can be downloaded prior to the end of the workflow, there is a few seconds where an attacker can extract the token from the artifact and use it with the Github API to push malicious code or rewrite release commits in the AdeptLanguage/Adept repository. This issue has been patched in commit a1a41b7.
- 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.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.