GSD-2021-44451
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
Apache Superset up to and including 1.3.2 allowed for registered database connections password leak for authenticated users. This information could be accessed in a non-trivial way. Users should upgrade to Apache Superset 1.4.0 or higher.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-44451",
"description": "Apache Superset up to and including 1.3.2 allowed for registered database connections password leak for authenticated users. This information could be accessed in a non-trivial way. Users should upgrade to Apache Superset 1.4.0 or higher.",
"id": "GSD-2021-44451"
},
"gsd": {
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"remediation": "unknown",
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"id": "GSD-2021-44451",
"modified": "2023-12-13T01:23:20.070200Z",
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"cwe_ids": [
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"date": "2022-02-05",
"description": "Apache Superset up to and including allowed for registered database connections password leak for authenticated users. This information could be accessed in a non-trivial way. Users should upgrade to Apache Superset or higher.",
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"package_slug": "pypi/apache-superset",
"pubdate": "2022-02-01",
"solution": "Upgrade to version 1.4.0 or above.",
"title": "Insufficiently Protected Credentials",
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"solution": "Unfortunately, there is no solution available yet.",
"title": "Insufficiently Protected Credentials",
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"impact": {
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"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "NONE",
"baseScore": 4.0,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:S/C:P/I:N/A:N",
"version": "2.0"
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"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
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"severity": "MEDIUM",
"userInteractionRequired": false
},
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"baseSeverity": "MEDIUM",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N",
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"impactScore": 3.6
}
},
"lastModifiedDate": "2022-02-05T02:06Z",
"publishedDate": "2022-02-01T14:15Z"
}
}
}
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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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