CVE-2025-6507
Published: 01 September 2025
Summary
CVE-2025-6507 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 9.8 (Critical).
Operationally, ranked in the top 35.0% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Machine Learning Libraries; in the Supply Chain and Deployment risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-28743
Vulnerability details
A vulnerability in the h2oai/h2o-3 repository allows attackers to exploit deserialization of untrusted data, potentially leading to arbitrary code execution and reading of system files. This issue affects the latest master branch version 3.47.0.99999. The vulnerability arises from the ability…
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to bypass regular expression filters intended to prevent malicious parameter injection in JDBC connections. Attackers can manipulate spaces between parameters to evade detection, allowing for unauthorized file access and code execution. The vulnerability is addressed in version 3.46.0.8.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: h2o
Related Threats
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.