GSD-2021-33649
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
When performing the inference shape operation of the Transpose operator, if the value in the perm element is greater than or equal to the size of the input_shape, it will access data outside of bounds of input_shape which allocated from heap buffers.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-33649",
"description": "When performing the inference shape operation of the Transpose operator, if the value in the perm element is greater than or equal to the size of the input_shape, it will access data outside of bounds of input_shape which allocated from heap buffers.",
"id": "GSD-2021-33649"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2021-33649"
],
"details": "When performing the inference shape operation of the Transpose operator, if the value in the perm element is greater than or equal to the size of the input_shape, it will access data outside of bounds of input_shape which allocated from heap buffers.",
"id": "GSD-2021-33649",
"modified": "2023-12-13T01:23:18.811230Z",
"schema_version": "1.4.0"
}
},
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"ID": "CVE-2021-33649",
"STATE": "PUBLIC"
},
"affects": {
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"product": {
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"product_name": "openEuler:mindspore",
"version": {
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{
"version_value": "\u003e= 0.7.0-beta, \u003c 1.3.0"
}
]
}
}
]
},
"vendor_name": "n/a"
}
]
}
},
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"data_type": "CVE",
"data_version": "4.0",
"description": {
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"value": "When performing the inference shape operation of the Transpose operator, if the value in the perm element is greater than or equal to the size of the input_shape, it will access data outside of bounds of input_shape which allocated from heap buffers."
}
]
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "CWE-125 Out-of-bounds Read"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://gitee.com/mindspore/community/blob/master/security/security_advisory_list/mssa-2021-006_en.md",
"refsource": "MISC",
"url": "https://gitee.com/mindspore/community/blob/master/security/security_advisory_list/mssa-2021-006_en.md"
}
]
}
},
"nvd.nist.gov": {
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"nodes": [
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"children": [],
"cpe_match": [
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"cpe23Uri": "cpe:2.3:a:mindspore:mindspore:*:*:*:*:*:openeuler:*:*",
"cpe_name": [],
"versionEndExcluding": "1.3.0",
"versionStartIncluding": "1.0.0",
"vulnerable": true
},
{
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"description": {
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}
]
},
"problemtype": {
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"name": "https://gitee.com/mindspore/community/blob/master/security/security_advisory_list/mssa-2021-006_en.md",
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"tags": [
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"url": "https://gitee.com/mindspore/community/blob/master/security/security_advisory_list/mssa-2021-006_en.md"
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},
"impact": {
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"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 5.0,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:N/A:N",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6
}
},
"lastModifiedDate": "2022-07-07T16:18Z",
"publishedDate": "2022-06-27T17:15Z"
}
}
}
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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.
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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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