Null pointer dereference via invalid Ragged Tensors
Impact
Calling tf.raw_ops.RaggedTensorToVariant with arguments specifying an invalid ragged tensor results in a null pointer dereference:
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
filter_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 56, 56, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 23, 1])
import tensorflow as tf
input_tensor = tf.constant([], shape=[2, 2, 2, 2, 0], dtype=tf.float32)
filter_tensor = tf.constant([], shape=[0, 0, 2, 6, 2], dtype=tf.float32)
tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 39, 34, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
The implementation of RaggedTensorToVariant operations does not validate that the ragged tensor argument is non-empty:
int ragged_rank = batched_ragged.ragged_rank();
auto batched_splits_top_vec = batched_ragged.splits(0).vec<SPLIT_TYPE>();
Since batched_ragged contains no elements, batched_ragged.splits is a null vector, thus batched_ragged.splits(0) will result in dereferencing nullptr.
Patches
We have patched the issue in GitHub commit b055b9c474cd376259dde8779908f9eeaf097d93.
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-84mw-34w6-2q43
- https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93
- https://nvd.nist.gov/vuln/detail/CVE-2021-29516
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-444.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-642.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-153.yaml
- Vector CVSS
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Debilidad (CWE)
CWE-476- 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-2021-153 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-444 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-642 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-84mw-34w6-2q43 - 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
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Null pointer dereference via invalid Ragged Tensors esta entrada
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