CVE-2025-62156 (GCVE-0-2025-62156)
Vulnerability from cvelistv5 – Published: 2025-10-14 14:52 – Updated: 2025-10-14 16:04
VLAI
EPSS
VEX
Title
argo-workflows Zip Slip path traversal allows arbitrary file write and container configuration overwrite
Summary
Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Versions prior to 3.6.12 and versions 3.7.0 through 3.7.2 contain a Zip Slip path traversal vulnerability in artifact extraction. During artifact extraction the unpack/untar logic (workflow/executor/executor.go) uses filepath.Join(dest, filepath.Clean(header.Name)) without validating that header.Name stays within the intended extraction directory. A malicious archive entry can supply a traversal or absolute path that, after cleaning, overrides the destination directory and causes files to be written outside the /work/tmp extraction path and into system directories such as /etc inside the container. The vulnerability enables arbitrary file creation or overwrite in system configuration locations (for example /etc/passwd, /etc/hosts, /etc/crontab), which can lead to privilege escalation or persistence within the affected container. Update to 3.6.12 or 3.7.3 to remediate the issue.
Severity
8.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-14 16:04 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/argoproj/argo-workflows/securi… | x_refsource_CONFIRM |
| https://github.com/argoproj/argo-workflows/commit… | x_refsource_MISC |
| https://github.com/argoproj/argo-workflows/commit… | x_refsource_MISC |
| https://github.com/argoproj/argo-workflows/blob/9… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| argoproj | argo-workflows |
Affected:
< 3.6.12
Affected: >= 3.7.0, < 3.7.3 |
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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