Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-64164 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Dataease Dataease. Its CVSS base score is 8.9 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 45th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-64164 is a Java Naming and Directory Interface (JNDI) injection vulnerability in DataEase, an open source data visualization analysis tool. The flaw affects versions 2.10.14 and below, stemming from improper input filtering when establishing JDBC connections to Oracle databases. Published on 2025-11-06, it carries a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H) and maps to CWE-502.
Unauthenticated attackers can exploit this vulnerability remotely over the network with low attack complexity and no user interaction. By supplying malicious input during the JDBC connection process to Oracle, they can trigger JNDI injection, enabling high-impact compromise of confidentiality, integrity, and availability on the affected DataEase instance.
The vulnerability is fixed in DataEase version 2.10.15. Mitigation involves upgrading to this patched release. Key resources include the fixing commit at https://github.com/dataease/dataease/commit/7b68eb3dfccbbd12ec977e6320dbd3e32a7bbfe6, the v2.10.15 release notes at https://github.com/dataease/dataease/releases/tag/v2.10.15, and the GitHub security advisory at https://github.com/dataease/dataease/security/advisories/GHSA-q754-4pc2-wjqw.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-37957
Vulnerability Data
Dataease is an open source data visualization analysis tool. In versions 2.10.14 and below, DataEase did not properly filter when establishing JDBC connections to Oracle, resulting in a risk of JNDI injection (Java Naming and Directory Interface injection). This issue…
more
is fixed in version 2.10.15.
- 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.