CVE-2022-29241
Published: 14 June 2022
Summary
CVE-2022-29241 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Jupyter Jupyter Server. Its CVSS base score is 7.1 (High).
Operationally, ranked in the top 49.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-0132
Vulnerability details
Jupyter Server provides the backend (i.e. the core services, APIs, and REST endpoints) for Jupyter web applications like Jupyter Notebook. Prior to version 1.17.1, if notebook server is started with a value of `root_dir` that contains the starting user's home…
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directory, then the underlying REST API can be used to leak the access token assigned at start time by guessing/brute forcing the PID of the jupyter server. While this requires an authenticated user session, this URL can be used from a cross-site scripting payload or from a hooked or otherwise compromised browser to leak this access token to a malicious third party. This token can be used along with the REST API to interact with Jupyter services/notebooks such as modifying or overwriting critical files, such as .bashrc or .ssh/authorized_keys, allowing a malicious user to read potentially sensitive data and possibly gain control of the impacted system. This issue is patched in version 1.17.1.
- 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.