AI Sec Watch

TensorFlow (an open source machine learning platform) has a vulnerability where an attacker can cause a null pointer dereference (accessing memory that doesn't exist, crashing the program) by providing invalid input to a specific function called `tf.raw_ops.SparseMatrixSparseCholesky`. The problem occurs because the code fails to properly validate inputs due to a macro that returns early from a validation function without stopping the main code from continuing.

TensorFlow has a heap buffer overflow vulnerability (a memory access bug where data is written beyond allocated space) in its image resizing function that can be triggered by specially crafted input values causing incorrect array index calculations. An attacker can exploit this by manipulating floating-point numbers so that rounding errors cause the function to access memory outside the intended image data.

TensorFlow, an open source platform for machine learning, has a vulnerability where an attacker can cause a division by zero error in the `tf.raw_ops.QuantizedMul` function by controlling a value used in a division operation. This crash could disrupt systems using the affected code.

TensorFlow, an open source machine learning platform, has a vulnerability where an attacker can cause a division by zero error (crashing the program by dividing by zero) in the `tf.raw_ops.QuantizedConv2D` function by controlling a value that the code divides by. This happens because the code doesn't check if that value is zero before using it in math.

TensorFlow, a machine learning platform, has a vulnerability where an attacker can cause a division by zero error in the Conv2D function (a tool that processes image data) by controlling certain input values. This crash occurs because the code divides by a number that comes directly from the attacker's input without checking if it's zero first.

TensorFlow, a machine learning platform, has a vulnerability where an attacker can cause a division by zero error in a specific function called `tf.raw_ops.Conv2DBackpropInput` by controlling certain input values. This happens because the code divides by a number that comes from the attacker's input without checking if it's zero first.

TensorFlow, an open source machine learning platform, has a vulnerability where an attacker can cause a division by zero error (a crash caused by attempting math with zero as a divisor) in a specific function called `tf.raw_ops.Conv2DBackpropFilter` by controlling a value used in a modulus operation (a calculation that finds remainders). This bug affects multiple older versions of the software.

TensorFlow (an open source machine learning platform) has a vulnerability where an attacker can crash the program through a denial of service attack by sending malicious input to the `AddManySparseToTensorsMap` function. The problem occurs because the code uses an outdated constructor method that fails abruptly when it encounters numeric overflow (when a number gets too large for the system to handle), rather than handling the error gracefully.

A bug in TensorFlow (an open source machine learning platform) allows attackers to cause a denial of service (making a system unavailable) by triggering a division by zero error in the `tf.raw_ops.Conv3DBackprop*` operations. The operations don't check if input tensors are empty before using them in calculations, which crashes the system if an attacker controls the input sizes.