Headline
CVE-2022-35992: Check for element_shape in TensorListFromTensor · tensorflow/tensorflow@3db59a0
TensorFlow is an open source platform for machine learning. When TensorListFromTensor receives an element_shape of a rank greater than one, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 3db59a042a38f4338aa207922fa2f476e000a6ee. 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.
@@ -584,6 +584,17 @@ def testTensorListFromTensor(self): self.assertAllEqual(e, 1.0) self.assertAllEqual(list_ops.tensor_list_length(l), 0)
def testTensorListFromTensorFailsWhenElementShapeIsNotVector(self): t = constant_op.constant([1.0, 2.0]) # In Eager mode, InvalidArgumentError is generated by the Compute function. # In graph mode, ValueError is generated by the shape function. with self.assertRaisesRegex( (errors.InvalidArgumentError, ValueError), “must be at most rank 1”): # Wrong element_shape. Should be at most rank 1. l = list_ops.tensor_list_from_tensor(t, element_shape=[[1]]) self.evaluate(l)
@test_util.run_gpu_only def testFromTensorGPU(self): with context.device(“gpu:0”):
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### Impact When `TensorListFromTensor` receives an `element_shape` of a rank greater than one, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(6, 6, 2), dtype=tf.bfloat16, maxval=None) arg_1=tf.random.uniform(shape=(6, 9, 1, 3), dtype=tf.int64, maxval=65536) arg_2='' tf.raw_ops.TensorListFromTensor(tensor=arg_0, element_shape=arg_1, name=arg_2) ``` ### Patches We have patched the issue in GitHub commit [3db59a042a38f4338aa207922fa2f476e000a6ee](https://github.com/tensorflow/tensorflow/commit/3db59a042a38f4338aa207922fa2f476e000a6ee). 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/SECURITY.md) for more information regarding the securit...