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.
Software afectado
Haz clic en una tecnología para ver todas sus vulnerabilidades.
| 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/security/advisories/GHSA-xm2v-8rrw-w9pm
- https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535
- https://nvd.nist.gov/vuln/detail/CVE-2021-29525
- 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/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 15:17
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2021-162 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-453 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-651 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-xm2v-8rrw-w9pm - CC BY 4.0
Historial de vulnerabilidades: tensorflow-cpu
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
- 430
- Primera → última
- 04.2019 07.2024
- Sin corrección
- 0
- Explotada en ataques
- 0
Distribución por gravedad
- Crítica 6
- Alta 111
- Media 208
- Baja 105
Divulgaciones por año
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Division by 0 in `Conv2DBackpropInput` esta entrada
Identifica las vulnerabilidades de tu aplicación
Con la integración de DockRay y el inventario de software activado, DockRay compara las versiones notificadas de los componentes con las vulnerabilidades conocidas y avisa de las coincidencias confirmadas. La cobertura depende de la tecnología compatible y de los avisos disponibles.