CVE-2026-49984
Published: 26 June 2026
Summary
CVE-2026-49984 is a high-severity Path Traversal (CWE-22) vulnerability. Its CVSS base score is 7.7 (High).
Operationally, ranked at the 28.5th 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-2026-39920
Vulnerability details
Kestra is an open-source, event-driven orchestration platform. Prior to 1.0.45 and 1.3.23, the local internal-storage backend validates user-supplied paths for .. traversal before it converts Windows-style backslashes to forward slashes. An attacker can therefore smuggle a traversal sequence past the…
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guard using backslashes (..\..\..\); the guard sees a harmless string, and the path is only rewritten to ../../../ after validation, immediately before the file is opened. Any authenticated user who can view an execution (the lowest-privilege role) can call GET /api/v1/{tenant}/executions/{executionId}/file?path=… and read any file on the server filesystem readable by the Kestra process, outside the storage sandbox and across every tenant and namespace. This includes the embedded H2 database (all flows, all users, all stored secrets), internal storage of every other tenant/namespace, mounted secret files, and the process environment (/proc/self/environ) which contains configured database and secret-backend credentials. It is a complete breach of Kestra's storage isolation and multi-tenancy boundary. This vulnerability is fixed in 1.0.45 and 1.3.23.
- 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.