CVE-2025-62419
Published: 17 October 2025
Summary
CVE-2025-62419 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Dataease Dataease. Its CVSS base score is 8.2 (High).
Operationally, ranked at the 29.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-34919
Vulnerability details
DataEase is a data visualization and analytics platform. In DataEase versions through 2.10.13, a JDBC URL injection vulnerability exists in the DB2 and MongoDB data source configuration handlers. In the DB2 data source handler, when the extraParams field is empty,…
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the HOSTNAME, PORT, and DATABASE values are directly concatenated into the JDBC URL without filtering illegal parameters. This allows an attacker to inject a malicious JDBC string into the HOSTNAME field to bypass previously patched vulnerabilities CVE-2025-57773 and CVE-2025-58045. The vulnerability is fixed in version 2.10.14. No known workarounds exist.
- 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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.