Division by zero in Tensorflow
Impact
The estimator for the cost of some convolution operations can be made to execute a division by 0:
import tensorflow as tf
@tf.function
def test():
y=tf.raw_ops.AvgPoolGrad(
orig_input_shape=[1,1,1,1],
grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]],
ksize=[1,1,1,1],
strides=[1,1,1,0],
padding='VALID',
data_format='NCHW')
return y
test()The function fails to check that the stride argument is stricly positive:
int64_t GetOutputSize(const int64_t input, const int64_t filter,
const int64_t stride, const Padding& padding) {
// Logic for calculating output shape is from GetWindowedOutputSizeVerbose()
// function in third_party/tensorflow/core/framework/common_shape_fns.cc.
if (padding == Padding::VALID) {
return (input - filter + stride) / stride;
} else { // SAME.
return (input + stride - 1) / stride;
}
}Hence, the fix is to add a check for the stride argument to ensure it is valid.
Patches
We have patched the issue in GitHub commit 3218043d6d3a019756607643cf65574fbfef5d7a.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.
Affected software
Click a technology to see all vulnerabilities affecting it.
| Platform | Package | Vulnerable versions | Fixed in |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 | 2.5.3, 2.6.3, 2.7.1 |
| Python (PyPI) | tensorflow-cpu | < 2.5.3, >= 2.6.0, < 2.6.3 | 2.5.3, 2.6.3 |
| Python (PyPI) | tensorflow-gpu | < 2.5.3, >= 2.6.0, < 2.6.3 | 2.5.3, 2.6.3 |
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f
- https://nvd.nist.gov/vuln/detail/CVE-2022-21725
- https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-49.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-104.yaml
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198
- https://pypi.org/project/tensorflow
- https://github.com/advisories/GHSA-v3f7-j968-4h5f
- CVSS vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H- Weakness (CWE)
CWE-369- Platform
- Python (PyPI)
- Package
tensorflow, tensorflow-cpu, tensorflow-gpu- Disclosed
- 03.02.2022
- Updated
- 11.10.2026 14:18
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2022-49 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2022-104 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-v3f7-j968-4h5f - CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-3242 - PyPA Advisory Database; CC BY 4.0
Vulnerability history: tensorflow-gpu
All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.
- Total entries
- 427
- First → latest
- 04.2019 07.2024
- Without a fix
- 0
- Exploited in attacks
- 0
Severity breakdown
- Critical 6
- High 109
- Medium 207
- Low 105
Disclosures per year
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 18
- 2024: 1
- 2025: 0
- 2026: 0
-
Division by zero in Tensorflow this entry
Know which vulnerabilities affect your application
With the DockRay integration and software inventory enabled, DockRay checks reported component versions against known vulnerabilities and notifies you about confirmed matches. Coverage depends on the supported technology and available advisory data.