gsd-2021-37651
Vulnerability from gsd
Modified
2023-12-13 01:23
Details
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-37651",
"description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
"id": "GSD-2021-37651",
"references": [
"https://www.suse.com/security/cve/CVE-2021-37651.html",
"https://security.archlinux.org/CVE-2021-37651"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"details": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
"id": "GSD-2021-37651",
"modified": "2023-12-13T01:23:10.203231Z",
"schema_version": "1.4.0"
}
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"STATE": "PUBLIC",
"TITLE": "Heap buffer overflow in `FractionalAvgPoolGrad` in TensorFlow"
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"affects": {
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"value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 7.1,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N",
"version": "3.1"
}
},
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"value": "CWE-125: Out-of-bounds Read"
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{
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"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30"
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"source": {
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"discovery": "UNKNOWN"
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{
"affected_range": "\u003c2.3.4||\u003e=2.4.0,\u003c2.4.3||==2.5.0",
"affected_versions": "All versions before 2.3.4, all versions starting from 2.4.0 before 2.4.3, version 2.5.0",
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"cwe_ids": [
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"CWE-787",
"CWE-937"
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"date": "2021-08-25",
"description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
"fixed_versions": [
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"2.4.3",
"2.5.1"
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"identifier": "CVE-2021-37651",
"identifiers": [
"GHSA-hpv4-7p9c-mvfr",
"CVE-2021-37651"
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"not_impacted": "All versions starting from 2.3.4 before 2.4.0, all versions starting from 2.4.3 before 2.5.0, all versions after 2.5.0",
"package_slug": "pypi/tensorflow-cpu",
"pubdate": "2021-08-25",
"solution": "Upgrade to versions 2.3.4, 2.4.3, 2.5.1 or above.",
"title": "Out-of-bounds Write",
"urls": [
"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr",
"https://nvd.nist.gov/vuln/detail/CVE-2021-37651",
"https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30",
"https://github.com/advisories/GHSA-hpv4-7p9c-mvfr"
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"uuid": "5b209f7b-118f-40fa-9834-e7c9f6b49cc9"
},
{
"affected_range": "\u003c2.3.4||\u003e=2.4.0,\u003c2.4.3||==2.5.0",
"affected_versions": "All versions before 2.3.4, all versions starting from 2.4.0 before 2.4.3, version 2.5.0",
"cvss_v2": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
"cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"cwe_ids": [
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"CWE-787",
"CWE-937"
],
"date": "2021-08-25",
"description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
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"2.4.3",
"2.5.1"
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"identifier": "CVE-2021-37651",
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"package_slug": "pypi/tensorflow-gpu",
"pubdate": "2021-08-25",
"solution": "Upgrade to versions 2.3.4, 2.4.3, 2.5.1 or above.",
"title": "Out-of-bounds Write",
"urls": [
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"https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30",
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"cvss_v2": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
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"cwe_ids": [
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"date": "2021-08-18",
"description": "TensorFlow is an end-to-end open source platform for machine learning. The implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area.",
"fixed_versions": [
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"2.4.3"
],
"identifier": "CVE-2021-37651",
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"package_slug": "pypi/tensorflow",
"pubdate": "2021-08-12",
"solution": "Upgrade to versions 2.3.4, 2.4.3 or above.",
"title": "Out-of-bounds Write",
"urls": [
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"impact": {
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"authentication": "NONE",
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"vectorString": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
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"scope": "UNCHANGED",
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"exploitabilityScore": 1.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2021-08-18T14:46Z",
"publishedDate": "2021-08-12T21:15Z"
}
}
}
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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.
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