gsd-2021-29521
Vulnerability from gsd
Modified
2023-12-13 01:23
Details
TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-29521",
"description": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.",
"id": "GSD-2021-29521",
"references": [
"https://www.suse.com/security/cve/CVE-2021-29521.html",
"https://security.archlinux.org/CVE-2021-29521"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2021-29521"
],
"details": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.",
"id": "GSD-2021-29521",
"modified": "2023-12-13T01:23:36.776284Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
"cve.org": {
"CVE_data_meta": {
"ASSIGNER": "security-advisories@github.com",
"ID": "CVE-2021-29521",
"STATE": "PUBLIC",
"TITLE": "Segfault in SparseCountSparseOutput"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "tensorflow",
"version": {
"version_data": [
{
"version_value": "\u003c 2.3.3"
},
{
"version_value": "\u003e= 2.4.0, \u003c 2.4.2"
}
]
}
}
]
},
"vendor_name": "tensorflow"
}
]
}
},
"data_format": "MITRE",
"data_type": "CVE",
"data_version": "4.0",
"description": {
"description_data": [
{
"lang": "eng",
"value": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3."
}
]
},
"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-131: Incorrect Calculation of Buffer Size"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm",
"refsource": "CONFIRM",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm"
},
{
"name": "https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5"
}
]
},
"source": {
"advisory": "GHSA-hr84-fqvp-48mm",
"discovery": "UNKNOWN"
}
},
"gitlab.com": {
"advisories": [
{
"affected_range": "\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
"affected_versions": "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-131",
"CWE-937"
],
"date": "2021-05-21",
"description": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.",
"fixed_versions": [
"2.3.3",
"2.4.2"
],
"identifier": "CVE-2021-29521",
"identifiers": [
"GHSA-hr84-fqvp-48mm",
"CVE-2021-29521"
],
"not_impacted": "All versions 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 2.3.3, 2.4.2 or above.",
"title": "Incorrect Calculation of Buffer Size",
"urls": [
"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm",
"https://nvd.nist.gov/vuln/detail/CVE-2021-29521",
"https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5",
"https://github.com/advisories/GHSA-hr84-fqvp-48mm"
],
"uuid": "0e2fb005-f421-4b8a-ab5b-8aaeeaa97170"
},
{
"affected_range": "\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
"affected_versions": "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-131",
"CWE-937"
],
"date": "2021-05-21",
"description": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.",
"fixed_versions": [
"2.3.3",
"2.4.2"
],
"identifier": "CVE-2021-29521",
"identifiers": [
"GHSA-hr84-fqvp-48mm",
"CVE-2021-29521"
],
"not_impacted": "All versions 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 2.3.3, 2.4.2 or above.",
"title": "Incorrect Calculation of Buffer Size",
"urls": [
"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm",
"https://nvd.nist.gov/vuln/detail/CVE-2021-29521",
"https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5",
"https://github.com/advisories/GHSA-hr84-fqvp-48mm"
],
"uuid": "1e5031e5-489c-490f-a20c-c20b6ac7c971"
},
{
"affected_range": "\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
"affected_versions": "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-131",
"CWE-937"
],
"date": "2021-05-20",
"description": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e` data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.",
"fixed_versions": [
"2.5.0"
],
"identifier": "CVE-2021-29521",
"identifiers": [
"CVE-2021-29521",
"GHSA-hr84-fqvp-48mm"
],
"not_impacted": "All versions 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",
"pubdate": "2021-05-14",
"solution": "Upgrade to version 2.5.0 or above.",
"title": "Incorrect Calculation of Buffer Size",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2021-29521"
],
"uuid": "97be527f-dce1-4cc2-ae86-e0e4a344df09"
}
]
},
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"configurations": {
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"nodes": [
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"cpe_match": [
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"cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
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"versionEndExcluding": "2.3.3",
"versionStartIncluding": "2.3.0",
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{
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"value": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3."
}
]
},
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"references": {
"reference_data": [
{
"name": "https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5",
"refsource": "MISC",
"tags": [
"Patch",
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],
"url": "https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5"
},
{
"name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm",
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"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm"
}
]
}
},
"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": {
"cvssV3": {
"attackComplexity": "LOW",
"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": "2021-05-20T17:19Z",
"publishedDate": "2021-05-14T20:15Z"
}
}
}
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Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
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- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.