CVE-2024-56514
Jan. 3, 2025, 5:15 p.m.
None
No Score
Description
Karmada is a Kubernetes management system that allows users to run cloud-native applications across multiple Kubernetes clusters and clouds. Prior to version 1.12.0, both in karmadactl and karmada-operator, it is possible to supply a filesystem path, or an HTTP(s) URL to retrieve the custom resource definitions(CRDs) needed by Karmada. The CRDs are downloaded as a gzipped tarfile and are vulnerable to a TarSlip vulnerability. An attacker able to supply a malicious CRD file into a Karmada initialization could write arbitrary files in arbitrary paths of the filesystem. From Karmada version 1.12.0, when processing custom CRDs files, CRDs archive verification is utilized to enhance file system robustness. A workaround is available. Someone who needs to set flag `--crd` to customize the CRD files required for Karmada initialization when using `karmadactl init` to set up Karmada can manually inspect the CRD files to check whether they contain sequences such as `../` that would alter file paths, to determine if they potentially include malicious files. When using karmada-operator to set up Karmada, one must upgrade one's karmada-operator to one of the fixed versions.
Product(s) Impacted
Product | Versions |
---|---|
Karmada |
|
Weaknesses
CWE-22
Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.
References
Tags
Date
- Published: Jan. 3, 2025, 5:15 p.m.
- Last Modified: Jan. 3, 2025, 5:15 p.m.
Status : Awaiting Analysis
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
security-advisories@github.com
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.