gsd-2021-29517
Vulnerability from gsd
Modified
2023-12-13 01:23
Details
TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-29517",
"description": "TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
"id": "GSD-2021-29517",
"references": [
"https://www.suse.com/security/cve/CVE-2021-29517.html",
"https://security.archlinux.org/CVE-2021-29517"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2021-29517"
],
"details": "TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
"id": "GSD-2021-29517",
"modified": "2023-12-13T01:23:37.077916Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
"cve.org": {
"CVE_data_meta": {
"ASSIGNER": "security-advisories@github.com",
"ID": "CVE-2021-29517",
"STATE": "PUBLIC",
"TITLE": "Division by zero in `Conv3D`"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "tensorflow",
"version": {
"version_data": [
{
"version_value": "\u003c 2.1.4"
},
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"version_value": "\u003e= 2.2.0, \u003c 2.2.3"
},
{
"version_value": "\u003e= 2.3.0, \u003c 2.3.3"
},
{
"version_value": "\u003e= 2.4.0, \u003c 2.4.2"
}
]
}
}
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}
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"data_format": "MITRE",
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"data_version": "4.0",
"description": {
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{
"lang": "eng",
"value": "TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "HIGH",
"attackVector": "LOCAL",
"availabilityImpact": "LOW",
"baseScore": 2.5,
"baseSeverity": "LOW",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
"version": "3.1"
}
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "CWE-369: Divide By Zero"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv",
"refsource": "CONFIRM",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv"
},
{
"name": "https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f"
}
]
},
"source": {
"advisory": "GHSA-772p-x54p-hjrv",
"discovery": "UNKNOWN"
}
},
"gitlab.com": {
"advisories": [
{
"affected_range": "\u003c2.1.4||\u003e=2.2.0,\u003c2.2.3||\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
"affected_versions": "All versions before 2.1.4, all versions starting from 2.2.0 before 2.2.3, all versions starting from 2.3.0 before 2.3.3, all versions starting from 2.4.0 before 2.4.2",
"cvss_v2": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
"cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"cwe_ids": [
"CWE-1035",
"CWE-369",
"CWE-937"
],
"date": "2021-05-21",
"description": "TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
"fixed_versions": [
"2.1.4",
"2.2.3",
"2.3.3",
"2.4.2"
],
"identifier": "CVE-2021-29517",
"identifiers": [
"GHSA-772p-x54p-hjrv",
"CVE-2021-29517"
],
"not_impacted": "All versions starting from 2.1.4 before 2.2.0, all versions starting from 2.2.3 before 2.3.0, all versions starting from 2.3.3 before 2.4.0, all versions starting from 2.4.2",
"package_slug": "pypi/tensorflow-cpu",
"pubdate": "2021-05-21",
"solution": "Upgrade to versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 or above.",
"title": "Divide By Zero",
"urls": [
"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv",
"https://nvd.nist.gov/vuln/detail/CVE-2021-29517",
"https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f",
"https://github.com/advisories/GHSA-772p-x54p-hjrv"
],
"uuid": "018ba611-6f31-490b-ab89-318e96ed9f47"
},
{
"affected_range": "\u003c2.1.4||\u003e=2.2.0,\u003c2.2.3||\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
"affected_versions": "All versions before 2.1.4, all versions starting from 2.2.0 before 2.2.3, all versions starting from 2.3.0 before 2.3.3, all versions starting from 2.4.0 before 2.4.2",
"cvss_v2": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
"cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"cwe_ids": [
"CWE-1035",
"CWE-369",
"CWE-937"
],
"date": "2021-05-21",
"description": "TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
"fixed_versions": [
"2.1.4",
"2.2.3",
"2.3.3",
"2.4.2"
],
"identifier": "CVE-2021-29517",
"identifiers": [
"GHSA-772p-x54p-hjrv",
"CVE-2021-29517"
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"not_impacted": "All versions starting from 2.1.4 before 2.2.0, all versions starting from 2.2.3 before 2.3.0, all versions starting from 2.3.3 before 2.4.0, all versions starting from 2.4.2",
"package_slug": "pypi/tensorflow-gpu",
"pubdate": "2021-05-21",
"solution": "Upgrade to versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 or above.",
"title": "Divide By Zero",
"urls": [
"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv",
"https://nvd.nist.gov/vuln/detail/CVE-2021-29517",
"https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f",
"https://github.com/advisories/GHSA-772p-x54p-hjrv"
],
"uuid": "bff37476-8cb4-45ad-b09f-07a0ea728282"
},
{
"affected_range": "\u003c2.1.4||\u003e=2.2.0,\u003c2.2.3||\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
"affected_versions": "All versions before 2.1.4, all versions starting from 2.2.0 before 2.2.3, all versions starting from 2.3.0 before 2.3.3, all versions starting from 2.4.0 before 2.4.2",
"cvss_v2": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
"cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"cwe_ids": [
"CWE-1035",
"CWE-369",
"CWE-937"
],
"date": "2021-05-20",
"description": "TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash.",
"fixed_versions": [
"2.5.0"
],
"identifier": "CVE-2021-29517",
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"GHSA-772p-x54p-hjrv"
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"package_slug": "pypi/tensorflow",
"pubdate": "2021-05-14",
"solution": "Upgrade to version 2.5.0 or above.",
"title": "Divide By Zero",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2021-29517"
],
"uuid": "58838143-c76a-4e31-a249-192b34a4f0ec"
}
]
},
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"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv"
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{
"name": "https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f",
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},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 2.1,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "LOW",
"userInteractionRequired": false
},
"baseMetricV3": {
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"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 5.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 3.6
}
},
"lastModifiedDate": "2021-05-20T16:27Z",
"publishedDate": "2021-05-14T20:15Z"
}
}
}
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Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.