AI Sec Watch

TensorFlow, an open source machine learning platform, has a vulnerability in the `FakeQuantWithMinMaxVars` function where providing certain types of input tensors (multidimensional arrays of numbers) causes the program to crash, enabling a denial of service attack (making a system unavailable to users). The vulnerability has been identified and fixed in the codebase.

TensorFlow (an open source platform for machine learning) has a bug in the `QuantizedInstanceNorm` function where passing certain tensor inputs (`x_min` or `x_max` with nonzero rank, which are multi-dimensional arrays of numerical data) causes a segfault (a crash from accessing invalid memory), allowing attackers to trigger a denial of service attack (making the system unavailable). The vulnerability was fixed and will be released in TensorFlow 2.10.0, with backported patches for earlier versions.

TensorFlow (an open-source machine learning platform) has a bug in the `Conv2DBackpropInput` function where it crashes if the `input_sizes` parameter is not 4-dimensional, allowing attackers to cause a denial of service (making the system unavailable). The issue has been fixed and will be released in upcoming versions.

TensorFlow, an open source machine learning platform, has a bug in the `AvgPoolGrad` function where it doesn't properly check the input parameter `orig_input_shape`. This incomplete validation causes a CHECK failure (a crash that stops the program), which attackers can exploit to perform a denial of service attack (making the system unavailable to legitimate users).

TensorFlow, an open source machine learning platform, has a vulnerability in its `QuantizedAdd` function (a tool for adding quantized numbers, which are rounded values used to save memory). If this function receives certain tensor inputs of nonzero rank (multi-dimensional arrays), it crashes the program, which can be exploited to cause a denial of service attack (making the system unavailable to legitimate users).

A bug in TensorFlow (an open source platform for machine learning) called `QuantizedAvgPool` can crash when given certain types of inputs, allowing attackers to launch a denial of service attack (making a system unavailable). The issue has been fixed and will be released in upcoming versions of the software.

TensorFlow (an open source platform for machine learning) has a bug where the `LowerBound` or `UpperBound` functions crash if given an empty input list, causing a nullptr dereference (trying to access memory that doesn't exist). This crash can be exploited to launch a denial of service attack (making the system unavailable to legitimate users).

TensorFlow (an open source platform for machine learning) has a bug in the `BlockLSTMGradV2` function that doesn't properly check its inputs, allowing attackers to crash the system with a denial of service attack (causing the program to stop working). The vulnerability affects multiple versions of TensorFlow.

A bug in TensorFlow (an open source machine learning platform) within a function called `FractionalAvgPoolGrad` doesn't properly check its input data, causing an overflow (when a number becomes too large for the program to handle) that crashes the program and can be exploited to launch a denial of service attack (making a service unavailable to users).