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...

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...

Out of bounds access in tensorflow-lite

Impact In TensorFlow Lite, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of...

GHSA-CVPC-8PHH-8F45 Out of bounds access in tensorflow-lite

Impact In TensorFlow Lite, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of...

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-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...

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...

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...

Segfault and data corruption in tensorflow-lite

Impact To mimic Python's indexing with negative values, TFLite uses ResolveAxis to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds:...

GHSA-Q4QF-3FC6-8X34 Segfault and data corruption in tensorflow-lite

Impact To mimic Python's indexing with negative values, TFLite uses ResolveAxis to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds:...

GHSA-Q8GV-Q7WR-9JF8 Segfault in Tensorflow

Impact In eager mode, TensorFlow does not set the session state. Hence, calling tf.rawops.GetSessionHandle or tf.rawops.GetSessionHandleV2 results in a null pointer dereference:...

Segfault in Tensorflow

Impact In eager mode, TensorFlow does not set the session state. Hence, calling tf.rawops.GetSessionHandle or tf.rawops.GetSessionHandleV2 results in a null pointer dereference:...

Denial of Service in Tensorflow

Impact Changing the TensorFlow's SavedModel protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using tensorflow-serving or other inference-as-a-service installments. We have added...