Write to immutable memory region in TensorFlow
Impact
The tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area:
>>> import tensorflow as tf
>>> with open('/tmp/test.txt','w') as f: f.write('a'*128)
>>> tf.raw_ops.ImmutableConst(dtype=tf.string,shape=2,
memory_region_name='/tmp/test.txt')If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault.
This is because the alocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overriden.
Patches
We have patched the issue in GitHub commit c1e1fc899ad5f8c725dcbb6470069890b5060bc7 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
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 members of the Aivul Team from Qihoo 360.
Software afectado
Haz clic en una tecnología para ver todas sus vulnerabilidades.
| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 1.15.5, >= 2.0.0, < 2.0.4, >= 2.1.0, < 2.1.3, >= 2.2.0, < 2.2.2, >= 2.3.0, < 2.3.2 | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 |
| Python (PyPI) | tensorflow-cpu | < 1.15.5, >= 2.0.0, < 2.0.4, >= 2.1.0, < 2.1.3, >= 2.2.0, < 2.2.2, >= 2.3.0, < 2.3.2 | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 |
| Python (PyPI) | tensorflow-gpu | < 1.15.5, >= 2.0.0, < 2.0.4, >= 2.1.0, < 2.1.3, >= 2.2.0, < 2.2.2, >= 2.3.0, < 2.3.2 | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 |
Referencias
- https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hhvc-g5hv-48c6
- https://nvd.nist.gov/vuln/detail/CVE-2020-26268
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-299.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-334.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-255.yaml
- https://github.com/tensorflow/tensorflow
- Vector CVSS
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L- Debilidad (CWE)
CWE-471- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 10.12.2020
- Actualizado
- 11.10.2026 15:17
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2020-255 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-299 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-334 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-hhvc-g5hv-48c6 - 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
-
Write to immutable memory region in TensorFlow 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.