Division by 0 in `FractionalAvgPool`
Impact
An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.FractionalAvgPool:
import tensorflow as tf
value = tf.constant([60], shape=[1, 1, 1, 1], dtype=tf.int32)
pooling_ratio = [1.0, 1.0000014345305555, 1.0, 1.0]
pseudo_random = False
overlapping = False
deterministic = False
seed = 0
seed2 = 0
tf.raw_ops.FractionalAvgPool(
value=value, pooling_ratio=pooling_ratio, pseudo_random=pseudo_random,
overlapping=overlapping, deterministic=deterministic, seed=seed, seed2=seed2)
This is because the implementation computes a divisor quantity by dividing two user controlled values:
for (int i = 0; i < tensor_in_and_out_dims; ++i) {
output_size[i] = static_cast<int>(std::floor(input_size[i] / pooling_ratio_[i]));
DCHECK_GT(output_size[i], 0);
}
The user controls the values of input_size[i] and pooling_ratio_[i] (via the value.shape() and pooling_ratio arguments). If the value in input_size[i] is smaller than the pooling_ratio_[i], then the floor operation results in output_size[i] being 0. The DCHECK_GT line is a no-op outside of debug mode, so in released versions of TF this does not trigger.
Later, these computed values are used as arguments to GeneratePoolingSequence. There, the first computation is a division in a modulo operation:
std::vector<int64> GeneratePoolingSequence(int input_length, int output_length,
GuardedPhiloxRandom* generator,
bool pseudo_random) {
...
if (input_length % output_length == 0) {
diff = std::vector<int64>(output_length, input_length / output_length);
}
...
}
Since output_length can be 0, this results in runtime crashing.
Patches
We have patched the issue in GitHub commit 548b5eaf23685d86f722233d8fbc21d0a4aecb96.
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
Click a technology to see all vulnerabilities affecting it.
| 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/security/advisories/GHSA-f78g-q7r4-9wcv
- https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96
- https://nvd.nist.gov/vuln/detail/CVE-2021-29550
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-478.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-676.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-187.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 13:19
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2021-187 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-478 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-676 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-f78g-q7r4-9wcv - 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 0 in `FractionalAvgPool` this entry
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