CVE-2026-41586
Published: 07 May 2026
Summary
CVE-2026-41586 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 32.9th 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-2026-28316
Vulnerability details
Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. From versions 1.0.0 to 2.2.26, Channel.java implements readObject() and exposes deSerializeChannel() which call ObjectInputStream.readObject() on untrusted byte arrays without configuring an ObjectInputFilter. This is a classic…
more
Java deserialization RCE pattern. At time of publication, there are no publicly available patches.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Java deserialization RCE on untrusted input directly enables remote exploitation of a public-facing Fabric node (T1190).
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.