CVE-2024-21095
Published: 16 April 2024
Summary
CVE-2024-21095 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Oracle Primavera P6 Enterprise Project Portfolio Management. Its CVSS base score is 8.2 (High).
Operationally, ranked at the 44.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-18809
Vulnerability details
Vulnerability in the Primavera P6 Enterprise Project Portfolio Management product of Oracle Construction and Engineering (component: Web Access). Supported versions that are affected are 19.12.0-19.12.22, 20.12.0-20.12.21, 21.12.0-21.12.18, 22.12.0-22.12.12 and 23.12.0-23.12.2. Easily exploitable vulnerability allows unauthenticated attacker with network access via…
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HTTP to compromise Primavera P6 Enterprise Project Portfolio Management. Successful attacks of this vulnerability can result in unauthorized access to critical data or complete access to all Primavera P6 Enterprise Project Portfolio Management accessible data as well as unauthorized update, insert or delete access to some of Primavera P6 Enterprise Project Portfolio Management accessible data. CVSS 3.1 Base Score 8.2 (Confidentiality and Integrity impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:L/A:N).
- 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.