Division by 0 in `FusedBatchNorm`
Impact
An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.FusedBatchNorm:
import tensorflow as tf
x = tf.constant([], shape=[1, 1, 1, 0], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean,
variance=variance, epsilon=epsilon,
exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)This is because the implementation performs a division based on the last dimension of the x tensor:
const int depth = x.dimension(3);
const int rest_size = size / depth;Since this is controlled by the user, an attacker can trigger a denial of service.
Patches
We have patched the issue in GitHub commit 1a2a87229d1d61e23a39373777c056161eb4084d.
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.
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-r35g-4525-29fq
- https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d
- https://nvd.nist.gov/vuln/detail/CVE-2021-29555
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-483.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-681.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-192.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-2021-192 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-483 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-681 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-r35g-4525-29fq - CC BY 4.0
Historial de vulnerabilidades: tensorflow
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
- 433
- Primera → última
- 04.2019 07.2024
- Sin corrección
- 0
- Explotada en ataques
- 0
Distribución por gravedad
- Crítica 6
- Alta 112
- Media 210
- Baja 105
Divulgaciones por año
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 169
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Division by 0 in `FusedBatchNorm` 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.