CVE-2024-21624
Published: 09 February 2024
Summary
CVE-2024-21624 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Nonebot Nonebot. Its CVSS base score is 5.7 (Medium).
Operationally, ranked at the 42.1th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0119
Vulnerability details
nonebot2 is a cross-platform Python asynchronous chatbot framework written in Python. This security advisory pertains to a potential information leak (e.g., environment variables) in instances where developers utilize `MessageTemplate` and incorporate user-provided data into templates. The identified vulnerability has been…
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remedied in pull request #2509 and will be included in versions released from 2.2.0. Users are strongly advised to upgrade to these patched versions to safeguard against the vulnerability. A temporary workaround involves filtering underscores before incorporating user input into the message template.
- 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.