CVE-2023-42454
Published: 18 September 2023
Summary
CVE-2023-42454 is a critical-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Lovasoa Sqlpage. Its CVSS base score is 10.0 (Critical).
Operationally, ranked at the 27.4th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-2596
Vulnerability details
SQLpage is a SQL-only webapp builder. Someone using SQLpage versions prior to 0.11.1, whose SQLpage instance is exposed publicly, with a database connection string specified in the `sqlpage/sqlpage.json` configuration file (not in an environment variable), with the web_root is the…
more
current working directory (the default), and with their database exposed publicly, is vulnerable to an attacker retrieving database connection information from SQLPage and using it to connect to their database directly. Version 0.11.0 fixes this issue. Some workarounds are available. Using an environment variable instead of the configuration file to specify the database connection string prevents exposing it on vulnerable versions. Using a different web root (that is not a parent of the SQLPage configuration directory) fixes the issue. One should also avoid exposing one's database publicly.
- 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.