GSD-2022-38170
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
In Apache Airflow prior to 2.3.4, an insecure umask was configured for numerous Airflow components when running with the `--daemon` flag which could result in a race condition giving world-writable files in the Airflow home directory and allowing local users to expose arbitrary file contents via the webserver.
Aliases
Aliases
{
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"description": "In Apache Airflow prior to 2.3.4, an insecure umask was configured for numerous Airflow components when running with the `--daemon` flag which could result in a race condition giving world-writable files in the Airflow home directory and allowing local users to expose arbitrary file contents via the webserver.",
"id": "GSD-2022-38170"
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"id": "GSD-2022-38170",
"modified": "2023-12-13T01:19:22.290385Z",
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"value": "The Apache Airflow PMC would like to thank Harry Sintonen for reporting this issue."
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"value": "Run without the `--daemon` flag via a process supervisor instead (systemd, runit, etc.)."
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"cwe_ids": [
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"CWE-732",
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],
"date": "2023-08-08",
"description": "In Apache Airflow prior to 2.3.4, an insecure umask was configured for numerous Airflow components when running with the `--daemon` flag which could result in a race condition giving world-writable files in the Airflow home directory and allowing local users to expose arbitrary file contents via the webserver.",
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"package_slug": "pypi/apache-airflow",
"pubdate": "2022-09-02",
"solution": "Upgrade to version 2.3.4 or above.",
"title": "Concurrent Execution using Shared Resource with Improper Synchronization (\u0027Race Condition\u0027)",
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"impact": {
"baseMetricV3": {
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"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 4.7,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
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},
"lastModifiedDate": "2022-09-29T15:49Z",
"publishedDate": "2022-09-02T07:15Z"
}
}
}
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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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