CVE-2023-42820
Published: 27 September 2023
Summary
CVE-2023-42820 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Fit2Cloud Jumpserver. Its CVSS base score is 7.0 (High).
Operationally, ranked in the top 1.6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
Deeper analysis
JumpServer is an open source bastion host affected by CVE-2023-42820, a vulnerability that exposes the random number seed through the API. This exposure can allow replay of randomly generated verification codes and lead to password resets. The flaw is tracked under CWE-200 and carries a CVSS 3.1 score of 7.0; it does not affect users who have MFA enabled or who are not using local authentication.
An unauthenticated remote attacker can exploit the issue over the network, albeit with high attack complexity, to obtain limited confidentiality impact while achieving high integrity impact through unauthorized password changes.
The GitHub security advisory GHSA-7prv-g565-82qp and the linked commits advise upgrading to JumpServer 2.28.19 or 3.6.5, noting that no workarounds are available. The associated EPSS score has remained flat at its peak value of 0.6279 with no material rise after disclosure.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-47242
Vulnerability details
JumpServer is an open source bastion host. This vulnerability is due to exposing the random number seed to the API, potentially allowing the randomly generated verification codes to be replayed, which could lead to password resets. If MFA is enabled…
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users are not affect. Users not using local authentication are also not affected. Users are advised to upgrade to either version 2.28.19 or to 3.6.5. There are no known workarounds or 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.