CVE-2020-15197

TensorFlow prior to 2.3.1 is affected by CVE-2020-15197 due to a validation gap in SparseCountSparseOutput: the indices tensor is not checked to be rank 2, though code treats it as a matrix. This can allow crafted input sparse tensors to cause a CHECK failure and crash, enabling denial of service...

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...

CVE-2020-15198 Heap buffer overflow in Tensorflow

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...

CVE-2020-15198

CVE-2020-15198 affects TensorFlow up to 2.3.0: SparseCountSparseOutput may access heap buffers out of bounds due to missing validation that indices and values shapes match in a sparse tensor. This root cause enables a heap buffer overflow in pre-2.3.1 builds. A fix was committed (3cbb917b47147660...

CVE-2020-15198

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...

CVE-2020-15199 Denial of Service in Tensorflow

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-2020-15199

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-2020-15199

Summary: TensorFlow prior to 2.3.1 contains a bug in RaggedCountSparseOutput where input ragged tensors are not validated for proper splits; an empty or single-element splits can trigger a SIGABRT due to an initialization bound. Root cause: lack of validation in RaggedCountSparseOutput when formi...

CVE-2020-15200 Segfault in Tensorflow

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-2020-15200

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-2020-15200

CVE-2020-15200 affects TensorFlow before 2.3.1. The RaggedCountSparseOutput path does not validate that the input ragged tensor is well-formed, specifically not validating that the splits form a valid partition of values. This can set up conditions that lead to a heap-based buffer overflow and, i...

CVE-2020-15190

TensorFlow CVE-2020-15190 is a vulnerability in tf.raw_ops.Switch where, in eager mode, the runtime binds a reference to a nullptr when one of the two outputs is undefined. This causes undefined behavior and can segfault when compiled with -fsanitize=null. The issue affects TensorFlow versions 1....

CVE-2020-15190 Segfault in Tensorflow

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the tf.rawops.Switch operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. Howeve...

GHSA-HX2X-85GR-WRPQ Out of bounds access in tensorflow-lite

Impact In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor:...

Out of bounds access in tensorflow-lite

Impact In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor:...

GHSA-P2CQ-CPRG-FRVM Out of bounds write in tensorflow-lite

Impact In TensorFlow Lite models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor:...

Out of bounds write in tensorflow-lite

Impact In TensorFlow Lite models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor:...

GHSA-HJMQ-236J-8M87 Denial of service in tensorflow-lite

Impact In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very...

Denial of service in tensorflow-lite

Impact In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very...

GHSA-X9J7-X98R-R4W2 Segmentation fault in tensorflow-lite

Impact 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. Patches We have patched the issue in d58c96946b and will release patch releases for all versions between 1.1...