ghsa-79h2-q768-fpxr
Vulnerability from github
Published
2022-09-16 21:06
Modified
2022-09-19 19:00
Severity ?
Summary
TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions
Details
Impact
When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. ```python 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.
{
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"name": "tensorflow"
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],
"aliases": [
"CVE-2022-36027"
],
"database_specific": {
"cwe_ids": [
"CWE-20"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T21:06:31Z",
"nvd_published_at": "2022-09-16T23:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nWhen converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process.\n```python\nimport tensorflow as tf\n\nclass QuantConv2DTransposed(tf.keras.layers.Layer):\n def build(self, input_shape):\n self.kernel = self.add_weight(\"kernel\", [3, 3, input_shape[-1], 24])\n\n def call(self, inputs):\n filters = tf.quantization.fake_quant_with_min_max_vars_per_channel(\n self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True\n )\n filters = tf.transpose(filters, (0, 1, 3, 2))\n return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24], 1)\n\ninp = tf.keras.Input(shape=(6, 8, 48), batch_size=1)\nx = tf.quantization.fake_quant_with_min_max_vars(inp, -3.0, 3.0, narrow_range=True)\nx = QuantConv2DTransposed()(x)\nx = tf.quantization.fake_quant_with_min_max_vars(x, -3.0, 3.0, narrow_range=True)\n\nmodel = tf.keras.Model(inp, x)\n\nmodel.save(\"/tmp/testing\")\nconverter = tf.lite.TFLiteConverter.from_saved_model(\"/tmp/testing\")\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\n\n# terminated by signal SIGSEGV (Address boundary error)\ntflite_model = converter.convert()\n```\n\n### Patches\nWe have patched the issue in GitHub commit [aa0b852a4588cea4d36b74feb05d93055540b450](https://github.com/tensorflow/tensorflow/commit/aa0b852a4588cea4d36b74feb05d93055540b450).\n\nThe 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.\n\n\n### For more information\nPlease 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.\n\n\n### Attribution\nThis vulnerability has been reported by Lukas Geiger via [Github issue](https://github.com/tensorflow/tensorflow/issues/53767).\n",
"id": "GHSA-79h2-q768-fpxr",
"modified": "2022-09-19T19:00:53Z",
"published": "2022-09-16T21:06:31Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-36027"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/issues/53767"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/aa0b852a4588cea4d36b74feb05d93055540b450"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": " TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions"
}
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