CVE-2021-27001 (GCVE-0-2021-27001)
Vulnerability from cvelistv5 – Published: 2021-10-19 14:11 – Updated: 2024-08-03 20:40
VLAI
EPSS
VEX
Summary
Clustered Data ONTAP versions 9.x prior to 9.5P18, 9.6P16, 9.7P16, 9.8P7 and 9.9.1P2 are susceptible to a vulnerability which could allow an authenticated privileged local attacker to arbitrarily modify Compliance-mode WORM data prior to the end of the retention period.
Severity
5.5 (Medium)
CWE
- Arbitrary Data Modification
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://security.netapp.com/advisory/ntap-20211018-0001 | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | Clustered Data ONTAP |
Affected:
9.x prior to 9.5P18, 9.6P16, 9.7P16, 9.8P7 and 9.9.1P2
|
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"matchCriteriaId": "2ABBF729-6A69-4CEC-9B3E-735C45D3069E",
"vulnerable": true
},
{
"criteria": "cpe:2.3:o:netapp:clustered_data_ontap:9.8:p3:*:*:*:*:*:*",
"matchCriteriaId": "2926492D-0059-41D3-8E94-063533F313F5",
"vulnerable": true
},
{
"criteria": "cpe:2.3:o:netapp:clustered_data_ontap:9.8:p5:*:*:*:*:*:*",
"matchCriteriaId": "8782904F-9A19-4738-81D5-8BCE04C519D9",
"vulnerable": true
},
{
"criteria": "cpe:2.3:o:netapp:clustered_data_ontap:9.9.1:-:*:*:*:*:*:*",
"matchCriteriaId": "2A6A96D7-45B2-46BE-8894-6DC3F565A8A4",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Clustered Data ONTAP versions 9.x prior to 9.5P18, 9.6P16, 9.7P16, 9.8P7 and 9.9.1P2 are susceptible to a vulnerability which could allow an authenticated privileged local attacker to arbitrarily modify Compliance-mode WORM data prior to the end of the retention period."
},
{
"lang": "es",
"value": "Clustered Data ONTAP versiones 9.x anteriores a 9.5P18, 9.6P16, 9.7P16, 9.8P7 y 9.9.1P2, son susceptibles de una vulnerabilidad que podr\u00eda permitir a un atacante local privilegiado y autenticado modificar arbitrariamente los datos WORM en modo de cumplimiento antes de que finalice el periodo de retenci\u00f3n"
}
],
"id": "CVE-2021-27001",
"lastModified": "2024-11-21T05:57:10.343",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "LOW",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 2.1,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:L/Au:N/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 5.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 3.6,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2021-10-19T15:15:07.587",
"references": [
{
"source": "security-alert@netapp.com",
"tags": [
"Vendor Advisory"
],
"url": "https://security.netapp.com/advisory/ntap-20211018-0001"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "https://security.netapp.com/advisory/ntap-20211018-0001"
}
],
"sourceIdentifier": "security-alert@netapp.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "NVD-CWE-noinfo"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T14:27:16+00:00",
"cve": "CVE-2021-27001",
"id": "CVE-2021-27001",
"initial_release_date": "2021-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "CVE-2021-27001",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-27001.json",
"version": "3"
}
}
}
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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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