CVE-2022-46163
Published: 10 January 2023
Summary
CVE-2022-46163 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Opensuse Travel Support Program. Its CVSS base score is 7.5 (High).
Operationally, ranked in the top 25.0% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-48994
Vulnerability details
Travel support program is a rails app to support the travel support program of openSUSE (TSP). Sensitive user data (bank account details, password Hash) can be extracted via Ransack query injection. Every deployment of travel-support-program below the patched version is…
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affected. The travel-support-program uses the Ransack library to implement search functionality. In its default configuration, Ransack will allow for query conditions based on properties of associated database objects [1]. The `*_start`, `*_end` or `*_cont` search matchers [2] can then be abused to exfiltrate sensitive string values of associated database objects via character-by-character brute-force (A match is indicated by the returned JSON not being empty). A single bank account number can be extracted with <200 requests, a password hash can be extracted with ~1200 requests, all within a few minutes. The problem has been patched in commit d22916275c51500b4004933ff1b0a69bc807b2b7. In order to work around this issue, you can also cherry pick that patch, however it will not work without the Rails 5.0 migration that was done in #150, which in turn had quite a few pull requests it depended on.
- 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.
Penetration testing uses SQL injection payloads against database interfaces, identifying and supporting fixes for SQL injection weaknesses.
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.