CVE-2026-59974 (GCVE-0-2026-59974)
Vulnerability from cvelistv5 – Published: 2026-09-16 16:10 – Updated: 2026-09-16 17:28
VLAI
EPSS
VEX
Title
Stanza: Zip Slip Path Traversal in Model/Resource Extraction
Summary
Stanza is a Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages. Prior to 1.14.0, stanza.resources.common.unzip in stanza/resources/common.py passes downloaded model and resource archives to zipfile.ZipFile.extractall without validating member paths, and the vulnerable extraction path is reachable through stanza.download and stanza.install_corenlp. A malicious archive containing parent-directory traversal entries can write outside the intended model directory, allowing files writable by the Stanza process to be overwritten and potentially enabling code execution through modified shell configuration, SSH authorization data, Python packages, or executable scripts. This issue is fixed in version 1.14.0.
Severity
7.8 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-16 17:27 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/stanfordnlp/stanza/security/ad… | x_refsource_CONFIRM |
| https://github.com/stanfordnlp/stanza/commit/a708… | x_refsource_MISC |
| https://github.com/stanfordnlp/stanza/releases/ta… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| stanfordnlp | stanza |
Affected:
< 1.14.0
|
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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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