CVE-2024-29199
Published: 26 March 2024
Summary
CVE-2024-29199 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Networktocode Nautobot. Its CVSS base score is 3.7 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Information Repositories (T1213); ranked at the 37.4th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0959
Vulnerability details
Nautobot is a Network Source of Truth and Network Automation Platform. A number of Nautobot URL endpoints were found to be improperly accessible to unauthenticated (anonymous) users. These endpoints will not disclose any Nautobot data to an unauthenticated user unless…
more
the Nautobot configuration variable EXEMPT_VIEW_PERMISSIONS is changed from its default value (an empty list) to permit access to specific data by unauthenticated users. This vulnerability is fixed in 1.6.16 and 2.1.9.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Unauthenticated access to Nautobot endpoints exposes API structure, modules (dcim/ipam/etc.), secrets providers, auth backends, and job logs, enabling data collection from information repositories, software discovery, and gathering of victim host software information.
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.