CVE-2026-41732
Published: 10 June 2026
Summary
CVE-2026-41732 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Vmware Spring For Apache Pulsar. 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 26.7th 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-2026-35909
Vulnerability details
JsonPulsarHeaderMapper matched type headers against trusted packages using a prefix check, meaning that trusting any package implicitly trusted all of its subpackages. Additionally, an empty trusted-packages configuration fell back to trusting all packages rather than applying a safe default allow-list.…
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Affected versions: Spring for Apache Pulsar 2.0.0 through 2.0.5; 1.2.0 through 1.2.17; 1.1.0 through 1.1.17.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
CWE-502 deserialization flaw in trusted package prefix check directly enables remote code execution via malicious serialized headers in Pulsar messaging flows.
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.