CVE-2026-77763 (GCVE-0-2026-77763)
Vulnerability from cvelistv5 – Published: 2026-08-21 11:05 – Updated: 2026-08-21 21:44
VLAI
EPSS
VEX
Title
JuiceFS Local Filestore Backend Joins Object Keys onto the Storage Root Without a Containment Check, Allowing Writes Outside the Configured Directory
Summary
The filestore backend in pkg/object/file.go, used for file:// stores and as a common juicefs sync destination, derived every operation's target from path(key), which returned either filepath.Join(d.root, key) or filepath.Clean(d.root + key) with no check that the result stayed beneath the root. Put, Get, Head, Delete, Chmod, Chown, Symlink and Readlink all consumed that value directly. Object keys enumerated from a source object store during a sync are not constrained the way local filesystem names are, so a key containing traversal segments causes juicefs to write attacker-supplied content to a path outside the intended local destination, and no error is returned. An operator syncing from a bucket whose contents they do not fully control, such as a shared or public bucket or one an attacker can write to, is therefore exposed to a file write at an attacker-influenced location. The fix changes path() to return an error and rejects any key whose resolved path escapes the root.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-21 21:32 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/juicedata/juicefs/issues/7401 | issue-tracking |
| https://github.com/juicedata/juicefs/commit/0bcd7… | patch |
| https://github.com/juicedata/juicefs/pull/7425 | issue-tracking |
| https://github.com/juicedata/juicefs/blob/v1.4.1/… | technical-description |
| https://github.com/juicedata/juicefs | product |
| https://www.vulncheck.com/advisories/juicefs-loca… | third-party-advisory |
Impacted products
Date Public
2026-08-15 00:00
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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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