GHSA-xm2v-8rrw-w9pm
Vulnerability from github
Impact
An attacker can trigger a division by 0 in tf.raw_ops.Conv2DBackpropInput:
```python import tensorflow as tf
input_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32) filter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32) out_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1], use_cudnn_on_gpu=True, padding='SAME', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1]) ```
This is because the implementation does a division by a quantity that is controlled by the caller:
cc
const size_t size_A = output_image_size * dims.out_depth;
const size_t size_B = filter_total_size * dims.out_depth;
const size_t size_C = output_image_size * filter_total_size;
const size_t work_unit_size = size_A + size_B + size_C;
...
const size_t shard_size =
use_parallel_contraction ? 1 :
(target_working_set_size + work_unit_size - 1) / work_unit_size;
Patches
We have patched the issue in GitHub commit 2be2cdf3a123e231b16f766aa0e27d56b4606535.
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.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
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{
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"name": "tensorflow-gpu"
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{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
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"introduced": "2.4.0"
},
{
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}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2021-29525"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T23:17:46Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32)\nfilter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32)\nout_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32)\n\ntf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor,\n out_backprop=out_backprop, strides=[1, 1, 1, 1],\n use_cudnn_on_gpu=True, padding=\u0027SAME\u0027,\n explicit_paddings=[], data_format=\u0027NHWC\u0027,\n dilations=[1, 1, 1, 1])\n``` \n \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller:\n\n```cc \n const size_t size_A = output_image_size * dims.out_depth; \n const size_t size_B = filter_total_size * dims.out_depth;\n const size_t size_C = output_image_size * filter_total_size;\n const size_t work_unit_size = size_A + size_B + size_C;\n ...\n const size_t shard_size =\n use_parallel_contraction ? 1 :\n (target_working_set_size + work_unit_size - 1) / work_unit_size;\n```\n\n### Patches\nWe have patched the issue in GitHub commit [2be2cdf3a123e231b16f766aa0e27d56b4606535](https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535).\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-xm2v-8rrw-w9pm",
"modified": "2024-10-30T22:09:34Z",
"published": "2021-05-21T14:21:51Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xm2v-8rrw-w9pm"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29525"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-453.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-651.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-162.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": "Division by 0 in `Conv2DBackpropInput`"
}
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.