CVE-2023-34235
Published: 25 July 2023
Summary
CVE-2023-34235 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Strapi Strapi. Its CVSS base score is 8.6 (High).
Operationally, ranked in the top 12.8% 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-2023-2021
Vulnerability details
Strapi is an open-source headless content management system. Prior to version 4.10.8, it is possible to leak private fields if one is using the `t(number)` prefix. Knex query allows users to change the default prefix. For example, if someone changes…
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the prefix to be the same as it was before or to another table they want to query, the query changes from `password` to `t1.password`. `password` is protected by filtering protections but `t1.password` is not protected. This can lead to filtering attacks on everything related to the object again, including admin passwords and reset-tokens. Version 4.10.8 fixes this issue.
- 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.