CVE-2023-46851
Published: 07 November 2023
Summary
CVE-2023-46851 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Apache Allura. Its CVSS base score is 4.9 (Medium).
Operationally, ranked in the top 45.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-51017
Vulnerability details
Allura Discussion and Allura Forum importing does not restrict URL values specified in attachments. Project administrators can run these imports, which could cause Allura to read local files and expose them. Exposing internal files then can lead to other exploits,…
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like session hijacking, or remote code execution. This issue affects Apache Allura from 1.0.1 through 1.15.0. Users are recommended to upgrade to version 1.16.0, which fixes the issue. If you are unable to upgrade, set "disable_entry_points.allura.importers = forge-tracker, forge-discussion" in your .ini config file.
- 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.
Directly implements checks on information inputs to reject invalid data before processing.
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.