AI Sec Watch

TensorFlow is a machine learning platform that had a bug where a function called `BCast::ToShape` would crash when given very large numbers (larger than an `int32`, which is a 32-bit integer) even though it was designed to handle even larger numbers called `int64`. This bug could be triggered by using the `tf.experimental.numpy.outer` function with large inputs.

TensorFlow, an open source machine learning platform, had a bug where passing quantized tensors (specially compressed numeric data) to certain functions caused the parsing code to fail silently and return a null pointer (empty reference) instead of the expected data. This could cause crashes or unexpected behavior in machine learning programs using affected TensorFlow functions.

TensorFlow, an open source machine learning platform, has a vulnerability in its `tf.image.generate_bounding_box_proposals` function when running on GPU. The function fails to validate that the `scores` input has the correct rank (dimension structure), which could cause problems. This is classified as improper input validation (CWE-20, where a program doesn't properly check if data meets required specifications).

TensorFlow's poisson loss function (a tool for measuring prediction errors in machine learning) crashes when certain input dimensions multiply together and exceed the limit of a 32-bit integer, causing a size mismatch during broadcast assignment (aligning data for computation). The vulnerability affects multiple versions of TensorFlow.

TensorFlow (an open source platform for machine learning) has a bug in the `tf.raw_ops.ImageProjectiveTransformV2` function where it overflows (uses more memory than available) when given a large output shape. This vulnerability was caused by an incorrect calculation of buffer size (the amount of memory needed to store data).

TensorFlow (an open source machine learning platform) has a vulnerability in the `tf.raw_ops.FusedResizeAndPadConv2D` function where a buffer overflow (a memory error where data exceeds available space) occurs when given very large tensor shapes. The bug stems from an incorrect buffer size calculation.

TensorFlow, an open source machine learning platform, has a bug where creating a numpy array (a data structure for storing numbers) with a specific shape (one dimension with zero elements and others summing to a large number) causes an error. The developers have created a fix and will release it in upcoming versions of TensorFlow.

TensorFlow, an open source machine learning platform, has a vulnerability in the `BaseCandidateSamplerOp` function that causes a heap OOB read (out-of-bounds read, where a program accesses memory it shouldn't) when it receives certain invalid input values. This is a memory safety bug that could allow attackers to read sensitive data from the program's memory.

TensorFlow (an open source platform for machine learning) has a bug where certain operations crash when they receive a different number of inputs than expected, which could cause the program to stop working. This vulnerability is classified as an out-of-bounds read (accessing memory outside the intended range).