Integer overflow in Tensorflow
Impact
The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness:
import tensorflow as tf
input = tf.constant([1,1],dtype=tf.qint32)
@tf.function
def test():
y = tf.raw_ops.Dequantize(
input=input,
min_range=[1.0],
max_range=[10.0],
mode='MIN_COMBINED',
narrow_range=False,
axis=2**31-1,
dtype=tf.bfloat16)
return y
test()The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes axis + 1, an attacker can trigger an integer overflow:
int axis = -1;
Status s = c->GetAttr("axis", &axis);
// ...
if (axis < -1) {
return errors::InvalidArgument("axis should be at least -1, got ",
axis);
}
// ...
if (axis != -1) {
ShapeHandle input;
TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), axis + 1, &input));
// ...
}Patches
We have patched the issue in GitHub commit b64638ec5ccaa77b7c1eb90958e3d85ce381f91b.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.
Software afectado
Haz clic en una tecnología para ver todas sus vulnerabilidades.
| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 | 2.5.3, 2.6.3, 2.7.1 |
| Python (PyPI) | tensorflow-cpu | < 2.5.3, >= 2.6.0, < 2.6.3 | 2.5.3, 2.6.3 |
| Python (PyPI) | tensorflow-gpu | < 2.5.3, >= 2.6.0, < 2.6.3 | 2.5.3, 2.6.3 |
Referencias
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw
- https://nvd.nist.gov/vuln/detail/CVE-2022-21727
- https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-51.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-106.yaml
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034
- https://pypi.org/project/tensorflow
- https://github.com/advisories/GHSA-c6fh-56w7-fvjw
- Vector CVSS
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H- Debilidad (CWE)
CWE-190- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 03.02.2022
- Actualizado
- 11.10.2026 14:17
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2022-51 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2022-106 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-c6fh-56w7-fvjw - CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-3159 - PyPA Advisory Database; 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
-
Integer overflow 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.