Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:NSummary
CVE-2025-30761 is a medium-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Oracle Jre. Its CVSS base score is 5.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 43th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21554
Vulnerability Data
Vulnerability in the Oracle Java SE, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: Scripting). Supported versions that are affected are Oracle Java SE: 8u451, 8u451-perf and 11.0.27; Oracle GraalVM Enterprise Edition: 21.3.14. Difficult to exploit vulnerability allows…
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unauthenticated attacker with network access via multiple protocols to compromise Oracle Java SE, Oracle GraalVM Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized creation, deletion or modification access to critical data or all Oracle Java SE, Oracle GraalVM Enterprise Edition accessible data. Note: This vulnerability can be exploited by using APIs in the specified Component, e.g., through a web service which supplies data to the APIs. This vulnerability also applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. CVSS 3.1 Base Score 5.9 (Integrity impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:N).
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.