CVE-2022-36027
When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process.
import tensorflow as tf class QuantConv2DTransposed(tf.keras.layers.Layer): def build(self, input_shape): self.kernel = self.add_weight("kernel", [3, 3, input_shape[-1], 24]) def call(self, inputs): filters = tf.quantization.fake_quant_with_min_max_vars_per_channel( self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True ) filters = tf.transpose(filters, (0, 1, 3, 2)) return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24], 1) inp = tf.keras.Input(shape=(6, 8, 48), batch_size=1) x = tf.quantization.fake_quant_with_min_max_vars(inp, -3.0, 3.0, narrow_range=True) x = QuantConv2DTransposed()(x) x = tf.quantization.fake_quant_with_min_max_vars(x, -3.0, 3.0, narrow_range=True) model = tf.keras.Model(inp, x) model.save("/tmp/testing") converter = tf.lite.TFLiteConverter.from_saved_model("/tmp/testing") converter.optimizations = [tf.lite.Optimize.DEFAULT] # terminated by signal SIGSEGV (Address boundary error) tflite_model = converter.convert()
We have patched the issue in GitHub commit aa0b852a4588cea4d36b74feb05d93055540b450.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.
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This vulnerability has been reported by Lukas Geiger via Github issue.