Invalid validation in `QuantizeAndDequantizeV2`
Impact
The validation in tf.raw_ops.QuantizeAndDequantizeV2 allows invalid values for axis argument:
import tensorflow as tf
input_tensor = tf.constant([0.0], shape=[1], dtype=float)
input_min = tf.constant(-10.0)
input_max = tf.constant(-10.0)
tf.raw_ops.QuantizeAndDequantizeV2(
input=input_tensor, input_min=input_min, input_max=input_max,
signed_input=False, num_bits=1, range_given=False, round_mode='HALF_TO_EVEN',
narrow_range=False, axis=-2)The validation uses || to mix two different conditions:
OP_REQUIRES(ctx,
(axis_ == -1 || axis_ < input.shape().dims()),
errors::InvalidArgument(...));If axis_ < -1 the condition in OP_REQUIRES will still be true, but this value of axis_ results in heap underflow. This allows attackers to read/write to other data on the heap.
Patches
We have patched the issue in GitHub commit c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9.
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/security/advisories/GHSA-mq5c-prh3-3f3h
- https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9
- https://nvd.nist.gov/vuln/detail/CVE-2021-29610
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-538.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-736.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-247.yaml
- https://github.com/tensorflow/tensorflow
- Vector CVSS
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L- Debilidad (CWE)
CWE-665, CWE-787- 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-2021-247 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-538 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-736 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-mq5c-prh3-3f3h - 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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Invalid validation in `QuantizeAndDequantizeV2` esta entrada
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