ghsa-j8qc-5fqr-52fp
Vulnerability from github
Impact
An attacker can cause a division by zero to occur in Conv2DBackpropFilter:
```python import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32) out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], use_cudnn_on_gpu=False, padding='SAME', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ```
This is because the implementation computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):
cc
const size_t size_A = output_image_size * filter_total_size;
const size_t size_B = output_image_size * dims.out_depth;
const size_t size_C = filter_total_size * dims.out_depth;
const size_t work_unit_size = size_A + size_B + size_C;
const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;
If all shapes are empty then work_unit_size is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service.
Patches
We have patched the issue in GitHub commit c570e2ecfc822941335ad48f6e10df4e21f11c96.
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 Yakun Zhang and Ying Wang of Baidu X-Team.
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],
"aliases": [
"CVE-2021-29538"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T22:30:42Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can cause a division by zero to occur in `Conv2DBackpropFilter`:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\nfilter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32)\nout_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\n\ntf.raw_ops.Conv2DBackpropFilter(\n input=input_tensor,\n filter_sizes=filter_sizes,\n out_backprop=out_backprop,\n strides=[1, 1, 1, 1],\n use_cudnn_on_gpu=False,\n padding=\u0027SAME\u0027,\n explicit_paddings=[],\n data_format=\u0027NHWC\u0027,\n dilations=[1, 1, 1, 1]\n)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):\n\n```cc\nconst size_t size_A = output_image_size * filter_total_size; \nconst size_t size_B = output_image_size * dims.out_depth;\nconst size_t size_C = filter_total_size * dims.out_depth;\nconst size_t work_unit_size = size_A + size_B + size_C;\nconst size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;\n```\n\nIf all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. \n\n### Patches\nWe have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).\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 Yakun Zhang and Ying Wang of Baidu X-Team.",
"id": "GHSA-j8qc-5fqr-52fp",
"modified": "2024-10-30T23:26:36Z",
"published": "2021-05-21T14:22:38Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29538"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-466.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-664.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-175.yaml"
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{
"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": "Division by zero in `Conv2DBackpropFilter`"
}
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.