Segmentation fault in tensorflow-lite
Impact
If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.
Patches
We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Workarounds
A potential workaround would be to add a custom Verifier to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).
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 discovered from a variant analysis of GHSA-cvpc-8phh-8f45.
Software afectado
Haz clic en una tecnología para ver todas sus vulnerabilidades.
| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 1.15.4, >= 2.0.0, < 2.0.3, >= 2.1.0, < 2.1.2, >= 2.2.0, < 2.2.1, >= 2.3.0, < 2.3.1 | 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 |
| Python (PyPI) | tensorflow-cpu | < 1.15.4, >= 2.0.0, < 2.0.3, >= 2.1.0, < 2.1.2, >= 2.2.0, < 2.2.1, >= 2.3.0, < 2.3.1 | 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 |
| Python (PyPI) | tensorflow-gpu | < 1.15.4, >= 2.0.0, < 2.0.3, >= 2.1.0, < 2.1.2, >= 2.2.0, < 2.2.1, >= 2.3.0, < 2.3.1 | 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 |
Referencias
- https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html
- https://nvd.nist.gov/vuln/detail/CVE-2020-15210
- https://github.com/tensorflow/tensorflow/commit/094329d0dcb8290bed2b1ee420934971f422c86d
- https://github.com/tensorflow/tensorflow/commit/1c8709b437fec10875b0cf271889afec9bbf582e
- https://github.com/tensorflow/tensorflow/commit/8c2092e9f9ef78b3f9060f8bf5ce7a49d1ccdc8f
- https://github.com/tensorflow/tensorflow/commit/f4159ccef23d11eb58ee4263beaaeac1be3343c7
- https://github.com/tensorflow/tensorflow/commit/f50a14b00560a383865c2273e4a9094add3888d5
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-290.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-325.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-133.yaml
- https://github.com/tensorflow/tensorflow
- Vector CVSS
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H- Debilidad (CWE)
CWE-20, CWE-787- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 25.09.2020
- Actualizado
- 11.10.2026 13:19
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2020-133 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-290 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-325 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-x9j7-x98r-r4w2 - 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
-
Segmentation fault in tensorflow-lite 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.