Security Bulletin: Tensor Flow security vulnerabilities with denial of service on IBM Watson Machine Learning Server

Summary TensorFlow is vulnerable to a denial of service .Remote attacker could exploit this vulnerability to cause a denial of service condition on IBM Watson Machine Learning Server Vulnerability Details CVEID: CVE-2020-15190 DESCRIPTION: TensorFlow is vulnerable to a denial of service, caused b...

PT-2020-16391 · Google · Tensorflow

Name of the Vulnerable Software and Affected Versions: TensorFlow versions prior to 1.15.5 TensorFlow versions prior to 2.0.4 TensorFlow versions prior to 2.1.3 TensorFlow versions prior to 2.2.2 TensorFlow versions prior to 2.3.2 TensorFlow versions prior to 2.4.0 Description: The tf.raw...

Google TensorFlow 缓冲区错误漏洞

Google TensorFlow is a suite of end-to-end open source platforms for machine learning from Google USA. Google TensorFlow suffers from a buffer overflow vulnerability that stems from a tensor buffer being populated with the default value for the type, but forgetting to initialize the quantized...

Arbitrary Code Execution

tensorlfow is vulnerable to arbitrary code execution. The SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor, allowing an attacker to execute arbitrary code on the host OS by causing a shape mismatch that can result in accesses outside of...

CVE-2020-15200

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 ...

CVE-2020-15193

In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.todlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing ...

CVE-2020-15196

In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified. In the sparse and ragged count weights a...

PYSEC-2020-313

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 the same shape as the values one. The values in these tensors are always accessed...

PYSEC-2020-324

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a nullptr buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one...

PYSEC-2020-131

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a DCHECK which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can...

PYSEC-2020-273

In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.todlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing ...

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-2020-277

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-2020-120

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-2020-279

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...

PYSEC-2020-133

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b a...

PYSEC-2020-312

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-2020-116

In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.todlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing ...

PYSEC-2020-289

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a nullptr buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one...

PYSEC-2020-120

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...