AI Sec Watch

TensorFlow, a machine learning platform, has a vulnerability in one of its functions (`tf.raw_ops.CTCBeamSearchDecoder`) that fails to check if input data is empty before processing it. When an attacker provides empty input, the software crashes (segmentation fault, which is when a program tries to read from memory it shouldn't access), causing a denial of service (making the system unavailable).

TensorFlow, an open source machine learning platform, has a vulnerability in the `tf.raw_ops.FractionalMaxPoolGrad` function that can crash the program when given empty input tensors (arrays of data with no elements). The bug occurs because the code doesn't properly check that input and output tensors are valid before processing them, which can be exploited to cause a denial of service attack (making the system unavailable).

TensorFlow, an open source machine learning platform, has a vulnerability in its `tf.raw_ops.MaxPoolGrad` function called a heap buffer overflow (a bug where a program writes data beyond the memory it's allowed to use). The vulnerability occurs because the code doesn't properly check that array indices are valid before accessing data, which could allow attackers to read or corrupt memory.

TensorFlow (an open-source machine learning platform) has a vulnerability in a function called `tf.raw_ops.FractionalAvgPoolGrad` that can cause a heap buffer overflow (a memory error where a program writes data beyond allocated space). The bug happens because the code doesn't properly check that input arguments have the correct size before processing them.

A vulnerability called CVE-2021-29577 exists in TensorFlow (an open source platform for machine learning) in a function called `tf.raw_ops.AvgPool3DGrad`. The function has a heap buffer overflow (a memory safety bug where code writes data beyond the limits of allocated memory), which happens because the code assumes two data structures called `orig_input_shape` and `grad` tensors (multi-dimensional arrays of data) have matching dimensions but doesn't actually verify this before proceeding.

TensorFlow, an open source platform for machine learning, has a vulnerability in a specific function called `tf.raw_ops.MaxPool3DGradGrad` that can cause a heap buffer overflow (a type of memory corruption where data overflows into adjacent memory). The problem occurs because the code doesn't properly check whether initialization completes successfully, leaving data in an invalid state.

A bug in TensorFlow (an open-source machine learning platform) in the `tf.raw_ops.ReverseSequence` function fails to check if input arguments are valid, allowing attackers to cause a denial of service (making the system crash or stop responding) through stack overflow (when a program uses too much memory on the call stack) or CHECK-failure (when an internal safety check fails). The vulnerability affects multiple recent versions of TensorFlow.

TensorFlow, an open-source machine learning platform, has a vulnerability in the `tf.raw_ops.MaxPool3DGradGrad` function where it doesn't check if input tensors (data structures that hold multi-dimensional arrays) are empty before accessing their contents. An attacker can provide empty tensors to cause a null pointer dereference (trying to access memory that doesn't exist), crashing the program or potentially executing malicious code.

TensorFlow, an open-source platform for machine learning, has a vulnerability in the `tf.raw_ops.MaxPoolGradWithArgmax` function where it divides by a batch dimension (a count of data samples) without first checking that the number is not zero. This can cause a division by zero error, which crashes the program or causes unexpected behavior.