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GSA_kwCzR0hTQS03OWgyLXE3NjgtZnB4cs4AAu2D

Moderate EPSS: 0.00519% (0.65874 Percentile) EPSS:
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TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions

Affected Packages Affected Versions Fixed Versions
pypi:tensorflow-gpu >= 2.9.0, < 2.9.1, >= 2.8.0, < 2.8.1, < 2.7.2 2.9.1, 2.8.1, 2.7.2
155 Dependent packages
11,499 Dependent repositories
249,188 Downloads last month

Affected Version Ranges

All affected versions

0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.8.0, 2.9.0

All unaffected versions

2.7.2, 2.7.3, 2.7.4, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu >= 2.9.0, < 2.9.1, >= 2.8.0, < 2.8.1, < 2.7.2 2.9.1, 2.8.1, 2.7.2
88 Dependent packages
2,483 Dependent repositories
832,868 Downloads last month

Affected Version Ranges

All affected versions

1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.8.0, 2.9.0

All unaffected versions

2.7.2, 2.7.3, 2.7.4, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0, 2.18.1, 2.19.0
pypi:tensorflow >= 2.9.0, < 2.9.1, >= 2.8.0, < 2.8.1, < 2.7.2 2.9.1, 2.8.1, 2.7.2
2,172 Dependent packages
73,755 Dependent repositories
21,825,433 Downloads last month

Affected Version Ranges

All affected versions

0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.8.0, 2.9.0

All unaffected versions

2.7.2, 2.7.3, 2.7.4, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0, 2.18.1, 2.19.0

Impact

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()

Patches

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.

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 Lukas Geiger via Github issue.

References:

Identifiers

GHSA-79h2-q768-fpxr

CVE-2022-36027

Risk

Severity

Moderate

EPSS

EPSS Percentage: 0.00519

EPSS Percentile: 0.65874

Classification

General

Source

GitHub Advisory Database

Origin

Unspecified

Repository

https://github.com/tensorflow/tensorflow

Date and time

Published

almost 3 years ago

Updated

over 2 years ago

API

JSON