CVE-2022-31177
Published: 01 August 2022
Summary
CVE-2022-31177 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Dpgaspar Flask-Appbuilder. Its CVSS base score is 2.7 (Low).
Operationally, ranked in the top 42.6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-0104
Vulnerability details
Flask-AppBuilder is an application development framework built on top of Flask python framework. In versions prior to 4.1.3 an authenticated Admin user could query other users by their salted and hashed passwords strings. These filters could be made by using…
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partial hashed password strings. The response would not include the hashed passwords, but an attacker could infer partial password hashes and their respective users. This issue has been fixed in version 4.1.3. Users are advised to upgrade. There are no known workarounds for 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.