CVE-2026-44881
Published: 28 May 2026
Summary
CVE-2026-44881 is a high-severity Link Following (CWE-59) vulnerability in Portainer Portainer. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 33.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
OWASP Top 10 for Web (2025)
EU & UK References
No EU or UK CSIRT advisories indexed for this CVE.
Vulnerability details
Portainer Community Edition is a lightweight service delivery platform for containerized applications that can be used to manage Docker, Swarm, Kubernetes and ACI environments. From 2.33.0 to before 2.33.8, 2.39.2, and 2.41.0, Portainer supports deploying stacks from Git repositories. When…
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a Git-backed stack is created or updated, Portainer clones the repository using go-git v5, which translates Git blob entries with mode 0o120000 (symlink) into real OS symlinks on the host filesystem via os.Symlink. The only entry blocked from becoming a symlink is .gitmodules; every other path is created as a symlink without validation. Portainer's GET /api/stacks/{id}/file endpoint then reads the stack entry point with os.ReadFile, which follows OS symlinks transparently. A repository containing docker-compose.yml as a symlink to an arbitrary filesystem path causes the symlink target's contents to be returned verbatim in the HTTP response. Any authenticated user with rights to create or update a Git-backed stack — the default configuration in Portainer CE — can read arbitrary files accessible to the Portainer process. This vulnerability is fixed in 2.33.8, 2.39.2, and 2.41.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Arbitrary file read via symlink following directly enables access to data from the local filesystem.
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.