97 matches found
PYSEC-2021-444
TensorFlow is an end-to-end open source platform for machine learning. Calling tf.rawops.RaggedTensorToVariant with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of RaggedTensorToVariant...
PYSEC-2021-218
TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in tf.rawops.CTCBeamSearchDecoder, an attacker can trigger denial of service via segmentation faults. The...
PYSEC-2021-651
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in tf.rawops.Conv2DBackpropInput. This is because the...
PYSEC-2021-220
TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.rawops.FusedBatchNorm is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The...
PYSEC-2021-719
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the BatchToSpaceNd TFLite operator is vulnerable to a division by zero...
CVE-2021-29520
TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to tf.rawops.Conv3DBackprop operations can result in heap buffer overflows. This is because the...
CVE-2021-29602
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the DepthwiseConv TFLite operator is vulnerable to a division by zero...
CVE-2021-29608
TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in tf.rawops.RaggedTensorToTensor, an attacker can exploit an undefined behavior if input arguments are empty. The...
Google TensorFlow 数字错误漏洞
Google TensorFlow is an end-to-end open source machine learning platform. A divide-by-zero error vulnerability exists in the tf.rawops.QuantizedConv2D implementation in TensorFlow versions prior to 2.5.0. No detailed vulnerability details are provided at this time...
PT-2021-18348 · Google · Tensorflow
Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 2.5.0 TensorFlow versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 are also affected Description: The implementation of the SpaceToBatchNd TFLite operator is vulnerable to a division by zero error. An attacker can craft a...
Google TensorFlow 安全漏洞
Google TensorFlow is a suite of end-to-end open source platforms for machine learning from Google USA. A security vulnerability exists in Google TensorFlow 2.4.2, 2.3.3, 2.2.3, 2.1.4, which stems from the absence of loops between nodes of a TFlite graph. No detailed vulnerability details are...
CVE-2020-15199
In Tensorflow before version 2.3.1, the RaggedCountSparseOutput does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the splits tensor has the minimum required number of elements. Code uses this quantity to initialize a different data...
CVE-2020-15197
In Tensorflow before version 2.3.1, the SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices tensor has rank 2. This tensor must be a matrix because code assumes its elements are access...
PYSEC-2020-281
In Tensorflow before version 2.3.1, the RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Hence, the code is...
PYSEC-2020-309
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the SparseFillEmptyRowsGrad implementation has incomplete validation of the shapes of its arguments. Although reverseindexmapt and gradvaluest are accessed in a similar pattern, only reverseindexmapt is validated to be of proper...
PYSEC-2020-280
In Tensorflow before version 2.3.1, the RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Thus, the code sets ...