CVE-2022-33318
Published: 20 July 2022
Summary
CVE-2022-33318 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Iconics Genesis64. Its CVSS base score is 9.8 (Critical).
Operationally, ranked in the top 18.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-36361
Vulnerability details
Deserialization of Untrusted Data vulnerability in Mitsubishi Electric GENESIS64 versions 10.97 to 10.97.1, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97 to 10.97.1, Mitsubishi Electric ICONICS Suite versions 10.97 to 10.97.1, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97…
more
to 10.97.1, Mitsubishi Electric GENESIS32 versions 9.7 and prior, Mitsubishi Electric Iconics Digital Solutions GENESIS32 versions 9.7 and prior, and Mitsubishi Electric MC Works64 versions 4.04E and prior allows a remote unauthenticated attacker to execute an arbitrary malicious code by sending specially crafted packets to the GENESIS64, ICONICS Suite, GENESIS32, or MC Works64 server.
- 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.