CVE-2023-50218
Published: 03 May 2024
Summary
CVE-2023-50218 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Inductiveautomation Ignition. Its CVSS base score is 8.8 (High).
Operationally, ranked in the top 2.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
Deeper analysis
CVE-2023-50218 is a deserialization of untrusted data vulnerability in the ModuleInvoke class of Inductive Automation Ignition. The flaw stems from insufficient validation of user-supplied data, enabling remote code execution on affected installations. It carries a CVSS 3.1 score of 8.8 and is tracked under CWE-502.
Authenticated remote attackers can exploit the issue to execute arbitrary code in the context of the SYSTEM account. No unauthenticated attack path is described.
Public advisories from Inductive Automation and the Zero Day Initiative document the vulnerability as ZDI-CAN-21624. The EPSS score has remained flat at 0.4896 with no material increase after disclosure.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-55040
Vulnerability details
Inductive Automation Ignition ModuleInvoke Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Inductive Automation Ignition. Authentication is required to exploit this vulnerability. The specific flaw exists within…
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the ModuleInvoke class. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of SYSTEM. Was ZDI-CAN-21624.
- 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.