Heap out of bounds access in MakeEdge in TensorFlow
Impact
Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality:
DataType src_out = src->output_type(output_index);
DataType dst_in = dst->input_type(input_index);
//...However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.
In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.
Patches
We have patched the issue in GitHub commit 0cc38aaa4064fd9e79101994ce9872c6d91f816b 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.
Software afectado
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| 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/security/advisories/GHSA-q263-fvxm-m5mw
- https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b
- https://nvd.nist.gov/vuln/detail/CVE-2020-26271
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-302.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-337.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-257.yaml
- Vector CVSS
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L- Debilidad (CWE)
CWE-125, CWE-908- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 10.12.2020
- Actualizado
- 11.10.2026 13:19
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2020-257 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-302 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2020-337 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-q263-fvxm-m5mw - 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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