Headline
CVE-2022-35989: Fix MaxPool crash on GPU for invalid filter size. · tensorflow/tensorflow@32d7bd3
TensorFlow is an open source platform for machine learning. When MaxPool receives a window size input array ksize with dimensions greater than its input tensor input, the GPU kernel gives a CHECK fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 32d7bd3defd134f21a4e344c8dfd40099aaf6b18. 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. There are no known workarounds for this issue.
@@ -772,6 +772,18 @@ def testMaxPoolEmptyInput(self, **kwargs): expected=[], **kwargs)
@parameterized.parameters( GetTestConfigsDicts(nn_ops.max_pool, gen_nn_ops.max_pool_v2)) @test_util.run_deprecated_v1 def testMaxPoolInvalidFilterSize(self, **kwargs): with self.cached_session(use_gpu=test.is_gpu_available()): t = constant_op.constant(1.0, shape=[1, 1, 1, 1]) with self.assertRaisesRegex( (errors_impl.InvalidArgumentError, ValueError), “Negative dimension size”): t = self.evaluate( nn_ops.max_pool(t, ksize=[1, 1, 2, 1], strides=1, padding="VALID"))
# Tests for DepthwiseMaxPooling on CPU only. @parameterized.parameters( GetTestConfigsDicts(
Related news
### Impact When `MaxPool` receives a window size input array `ksize` with dimensions greater than its input tensor `input`, the GPU kernel gives a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf import numpy as np input = np.ones([1, 1, 1, 1]) ksize = [1, 1, 2, 2] strides = [1, 1, 1, 1] padding = 'VALID' data_format = 'NCHW' tf.raw_ops.MaxPool(input=input, ksize=ksize, strides=strides, padding=padding, data_format=data_format) ``` ### Patches We have patched the issue in GitHub commit [32d7bd3defd134f21a4e344c8dfd40099aaf6b18](https://github.com/tensorflow/tensorflow/commit/32d7bd3defd134f21a4e344c8dfd40099aaf6b18). 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](https://github.com/tensorflow/tensorflow/blob/master/...