CVE-2025-58059
Published: 28 August 2025
Summary
CVE-2025-58059 is a critical-severity OS Command Injection (CWE-78) vulnerability. Its CVSS base score is 9.1 (Critical).
Operationally, ranked at the 25.0th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-26128
Vulnerability details
Valtimo is a platform for Business Process Automation. In versions before 12.16.0.RELEASE, and from 13.0.0.RELEASE to before 13.1.2.RELEASE, any admin that can create or modify and execute process-definitions could gain access to sensitive data or resources. This includes but is…
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not limited to: running executables on the application host, inspecting and extracting data from the host environment or application properties, spring beans (application context, database pooling). The following conditions have to be met in order to perform this attack: the user must be logged in, have the admin role, and must have some knowledge about running scripts via a the Camunda/Operator engine. Version 12.16.0 and 13.1.2 have been patched. It is strongly advised to upgrade. If no scripting is needed in any of the processes, it could be possible to disable it altogether via the ProcessEngineConfiguration. However, this workaround could lead to unexpected side-effects.
- 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.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.