Uninitialized memory access in TensorFlow

Impact Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen: cc struct QUInt8 QUInt8 /...

Float cast overflow undefined behavior

Impact When the boxes argument of tf.image.cropandresize has a very large value, the CPU kernel implementation receives it as a C++ nan floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. Patches We have patched the issue in...

scap-security-guide bug fix and enhancement update

The scap-security-guide project provides a guide for configuration of the system from the final system's security point of view. The guidance is specified in the Security Content Automation Protocol SCAP format and constitutes a catalog of practical hardening advice, linked to government...

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:...

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...

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...

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...

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:...

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...

Data leak in Tensorflow

Impact The datasplits argument of tf.rawops.StringNGrams lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory python tf.rawops.StringNGramsdata="aa", "bb", "cc", "dd", "ee", "ff", datasplits=0,8, separator=" ", ngramwidths=3,...

Denial of Service in Tensorflow

Impact By controlling the fill argument of tf.strings.asstring, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a printf call is constructed:...

Integer truncation in Shard API usage

Impact The Shard API in TensorFlow expects the last argument to be a function taking two int64 i.e., long long arguments: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/util/worksharder.hL59-L60 However, there are several places in TensorFlo...

Heap buffer overflow in Tensorflow

Impact 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, this code is prone to heap buffer overflow...

Segfault in Tensorflow

Impact 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 following code sets up conditions to...

Heap buffer overflow in Tensorflow

Impact The implementation of SparseFillEmptyRowsGrad uses a double indexing pattern: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparsefillemptyrowsop.ccL263-L269 It is possible for reverseindexmapi to be an index outside of bound...

Denial of Service in Tensorflow

Impact 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 structure:...

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:...

CVE-2020-1938

When using the Apache JServ Protocol AJP, care must be taken when trusting incoming connections to Apache Tomcat. Tomcat treats AJP connections as having higher trust than, for example, a similar HTTP connection. If such connections are available to an attacker, they can be exploited in ways that...

The Comprehensive Compliance Guide (Get Assessment Templates)

Complying with cyber regulations forms a significant portion of the CISO's responsibility. Compliance is, in fact, one of the major drivers in the purchase and implementation of new security products. But regulations come in multiple different colors and shapes – some are tailored to a specific...