GSD-2021-29547
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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-2021-29547",
"description": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat\u003cT\u003e()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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-2021-29547",
"references": [
"https://www.suse.com/security/cve/CVE-2021-29547.html",
"https://security.archlinux.org/CVE-2021-29547"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat\u003cT\u003e()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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-2021-29547",
"modified": "2023-12-13T01:23:36.658765Z",
"schema_version": "1.4.0"
}
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"ID": "CVE-2021-29547",
"STATE": "PUBLIC",
"TITLE": "Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`"
},
"affects": {
"vendor": {
"vendor_data": [
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"product": {
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"value": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat\u003cT\u003e()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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"
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},
"problemtype": {
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"description": [
{
"lang": "eng",
"value": "CWE-125: Out-of-bounds Read"
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"name": "https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b",
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},
{
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"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj"
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"source": {
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"discovery": "UNKNOWN"
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{
"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",
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"cwe_ids": [
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"CWE-125",
"CWE-937"
],
"date": "2021-05-21",
"description": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation assumes the inputs are not empty. If any of these inputs is empty, `.flat\u003cT\u003e` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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"
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"identifier": "CVE-2021-29547",
"identifiers": [
"GHSA-4fg4-p75j-w5xj",
"CVE-2021-29547"
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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-cpu",
"pubdate": "2021-05-21",
"solution": "Upgrade to versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 or above.",
"title": "Out-of-bounds Read",
"urls": [
"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj",
"https://nvd.nist.gov/vuln/detail/CVE-2021-29547",
"https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b",
"https://github.com/advisories/GHSA-4fg4-p75j-w5xj"
],
"uuid": "e55e8c4f-099f-4d5a-95dc-3b6d6203ee64"
},
{
"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": [
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"CWE-125",
"CWE-937"
],
"date": "2021-05-21",
"description": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation assumes the inputs are not empty. If any of these inputs is empty, `.flat\u003cT\u003e` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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": [
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"2.2.3",
"2.3.3",
"2.4.2"
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"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": "Out-of-bounds Read",
"urls": [
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"affected_versions": "All versions up to 2.1.4, all versions starting from 2.2.0 up to 2.2.3, all versions starting from 2.3.0 up to 2.3.3, all versions starting from 2.4.0 up to 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": [
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],
"date": "2021-07-27",
"description": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`.",
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"package_slug": "pypi/tensorflow",
"pubdate": "2021-05-14",
"solution": "Upgrade to version 2.5.0 or above.",
"title": "Out-of-bounds Read",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2021-29547"
],
"uuid": "058e7f6c-cffb-46fe-80d1-10d940c8fac5"
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"value": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat\u003cT\u003e()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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."
}
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"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"
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"exploitabilityScore": 3.9,
"impactScore": 2.9,
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"obtainOtherPrivilege": false,
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"severity": "LOW",
"userInteractionRequired": false
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"attackVector": "LOCAL",
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"baseScore": 5.5,
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"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"
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"exploitabilityScore": 1.8,
"impactScore": 3.6
}
},
"lastModifiedDate": "2021-07-27T17:25Z",
"publishedDate": "2021-05-14T20:15Z"
}
}
}
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Sightings
| Author | Source | Type | Date |
|---|
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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