CVE-2025-9571
Published: 10 December 2025
Summary
CVE-2025-9571 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 8.7 (High).
Operationally, ranked in the top 27.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-202400
Vulnerability details
A remote code execution (RCE) vulnerability exists in Google Cloud Data Fusion. A user with permissions to upload artifacts to a Data Fusion instance can execute arbitrary code within the core AppFabric component. This could allow the attacker to gain…
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control over the Data Fusion instance, potentially leading to unauthorized access to sensitive data, modification of data pipelines, and exploration of the underlying infrastructure. The following CDAP versions include the necessary update to protect against this vulnerability: * 6.10.6+ * 6.11.1+ Users must immediately upgrade to them, or greater ones, available at: https://github.com/cdapio/cdap-build/releases .
- 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.