Division by zero in `Conv2DBackpropFilter`
Impact
An attacker can cause a division by zero to occur in Conv2DBackpropFilter:
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(
input=input_tensor,
filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 1, 1, 1],
use_cudnn_on_gpu=False,
padding='SAME',
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1]
)This is because the implementation computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):
const size_t size_A = output_image_size * filter_total_size;
const size_t size_B = output_image_size * dims.out_depth;
const size_t size_C = filter_total_size * dims.out_depth;
const size_t work_unit_size = size_A + size_B + size_C;
const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;If all shapes are empty then work_unit_size is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service.
Patches
We have patched the issue in GitHub commit c570e2ecfc822941335ad48f6e10df4e21f11c96.
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.
Software afectado
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| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| 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 |
Referencias
- https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp
- https://nvd.nist.gov/vuln/detail/CVE-2021-29538
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-466.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-664.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-175.yaml
- https://github.com/tensorflow/tensorflow
- Vector CVSS
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Debilidad (CWE)
CWE-369- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 14.05.2021
- Actualizado
- 11.10.2026 13:19
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2021-175 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-466 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-664 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-j8qc-5fqr-52fp - CC BY 4.0
Historial de vulnerabilidades: tensorflow-gpu
Todas las vulnerabilidades publicadas de este paquete (Python (PyPI)), de la más reciente a la más antigua. Muestra si es un fallo aislado o un paquete que hay que vigilar.
- Entradas en total
- 427
- Primera → última
- 04.2019 07.2024
- Sin corrección
- 0
- Explotada en ataques
- 0
Distribución por gravedad
- Crítica 6
- Alta 109
- Media 207
- Baja 105
Divulgaciones por año
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 18
- 2024: 1
- 2025: 0
- 2026: 0
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Division by zero in `Conv2DBackpropFilter` esta entrada
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