CVE-2026-41699
Published: 11 June 2026
Summary
CVE-2026-41699 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Vmware Spring For Graphql. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 34.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-36212
Vulnerability details
Spring for GraphQL applications are vulnerable to Unsafe Deserialization when processing paginated GraphQL queries. An attacker can craft a malicious GraphQL request that can lead to Remote Code Execution when the application exposes a paginated (Connection) field and the classpath…
more
contains specific classes that can be leveraged during deserialization. Affected versions: Spring for GraphQL 2.0.0 through 2.0.3; 1.4.0 through 1.4.5; 1.3.0 through 1.3.8.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Unsafe deserialization (CWE-502) in a public-facing Spring GraphQL endpoint directly enables remote code execution via crafted paginated queries.
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.