GHSA-79FV-9865-4QCV
Vulnerability from github – Published: 2021-05-21 14:26 – Updated: 2024-11-01 17:12Impact
The implementation of tf.raw_ops.MaxPoolGrad is vulnerable to a heap buffer overflow:
import tensorflow as tf
orig_input = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
ksize = [1, 1, 1, 1]
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.MaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
strides=strides, padding=padding, explicit_paddings=[])
The implementation fails to validate that indices used to access elements of input/output arrays are valid:
for (int index = out_start; index < out_end; ++index) {
int input_backprop_index = out_arg_max_flat(index);
FastBoundsCheck(input_backprop_index - in_start, in_end - in_start);
input_backprop_flat(input_backprop_index) += out_backprop_flat(index);
}
Whereas accesses to input_backprop_flat are guarded by FastBoundsCheck, the indexing in out_backprop_flat can result in OOB access.
Patches
We have patched the issue in GitHub commit a74768f8e4efbda4def9f16ee7e13cf3922ac5f7.
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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
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"ranges": [
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"events": [
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{
"package": {
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"package": {
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{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
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"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
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"ranges": [
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{
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],
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]
}
],
"aliases": [
"CVE-2021-29579"
],
"database_specific": {
"cwe_ids": [
"CWE-119",
"CWE-787"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T18:02:34Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nThe implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow:\n \n```python\nimport tensorflow as tf\n\norig_input = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)\norig_output = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)\ngrad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\nksize = [1, 1, 1, 1] \nstrides = [1, 1, 1, 1]\npadding = \"SAME\"\n\ntf.raw_ops.MaxPoolGrad(\n orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,\n strides=strides, padding=padding, explicit_paddings=[])\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid:\n\n```cc\nfor (int index = out_start; index \u003c out_end; ++index) {\n int input_backprop_index = out_arg_max_flat(index);\n FastBoundsCheck(input_backprop_index - in_start, in_end - in_start);\n input_backprop_flat(input_backprop_index) += out_backprop_flat(index);\n}\n```\n\nWhereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access.\n\n### Patches\nWe have patched the issue in GitHub commit [a74768f8e4efbda4def9f16ee7e13cf3922ac5f7](https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7).\n\nThe 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.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.",
"id": "GHSA-79fv-9865-4qcv",
"modified": "2024-11-01T17:12:52Z",
"published": "2021-05-21T14:26:23Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79fv-9865-4qcv"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29579"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-507.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-705.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-216.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Heap buffer overflow in `MaxPoolGrad`"
}
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