CVE-2024-20955
Published: 16 January 2024
Summary
CVE-2024-20955 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Oracle Graalvm. Its CVSS base score is 3.7 (Low).
Operationally, ranked in the top 33.3% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-18669
Vulnerability details
Vulnerability in the Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: Compiler). Supported versions that are affected are Oracle GraalVM for JDK: 17.0.9, 21.0.1; Oracle GraalVM Enterprise Edition: 20.3.12, 21.3.8 and 22.3.4. Difficult to…
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exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized read access to a subset of Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition accessible data. CVSS 3.1 Base Score 3.7 (Confidentiality impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:N/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.