CVE-2020-15213 Denial of service in tensorflow-lite

In TensorFlow Lite before versions 2.2.1 and 2.3.1, 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,...

CVE-2020-15201

CVE-2020-15201 : TensorFlow before 2.3.1 contains a bounds-checking flaw in RaggedCountSparseOutput, where input ragged-tensor validation is missing. Specifically, values in the splits tensor may not form a valid partitioning of values, risking a heap-based buffer overflow if split_values does no...

CVE-2020-15201

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...

CVE-2020-15208

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 c...

CVE-2020-15209

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...

CVE-2020-15210

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...

CVE-2020-15210 Segmentation fault in tensorflow-lite

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...

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...

CVE-2020-15197 Denial of Service 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 rank 2. This tensor must be a matrix because code assumes its elements are access...

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...

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-2020-15190

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-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...

GHSA-QH32-6JJC-QPRM Null pointer dereference in tensorflow-lite

Impact 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. The runtime assumes that these buffers are written to before a...

Null pointer dereference in tensorflow-lite

Impact 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. The runtime assumes that these buffers are written to before a...

GHSA-MXJJ-953W-2C2V Data corruption in tensorflow-lite

Impact When determining the common dimension size of two tensors, TFLite uses a DCHECK which is no-op outside of debug compilation modes: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.hL437-L442 Since the function...

GHSA-QC53-44CJ-VFVX Denial of Service in Tensorflow

Impact 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 accessed as elements of a matrix:...

Heap buffer overflow in Tensorflow

Impact 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 in parallel:...