AI Sec Watch

TensorFlow, an open-source machine learning platform, has a vulnerability where TFlite graphs (computational structures that define ML models) were not properly checked to prevent loops between nodes. An attacker could create malicious models that cause infinite loops or stack overflow (running out of memory from too many nested function calls) during model evaluation, potentially crashing the system.

TensorFlow (an open source machine learning platform) has a vulnerability in its `Minimum` and `Maximum` operators that can allow reading data outside the bounds of allocated memory if one of the input tensors is empty, because the broadcasting implementation (the process of making tensors compatible for operations) doesn't check whether array indexes are valid. This is a memory access bug that could expose sensitive data.

TensorFlow, an open source machine learning platform, has a vulnerability in its GatherNd operator (a function that gathers data from a tensor, or multi-dimensional array) where an attacker can cause a division by zero error (a crash caused by dividing by zero) by crafting a malicious model with an empty input. This could allow an attacker to crash or disrupt applications using this operator.

TensorFlow, an open source machine learning platform, has a vulnerability in its `TransposeConv` operator (a neural network layer that reshapes data) where a division by zero error can occur if an attacker creates a malicious model with stride values set to 0. This bug could cause the software to crash or behave unexpectedly when processing such a model.

TensorFlow, an open-source machine learning platform, has a vulnerability in its `SpaceToDepth` operator (a tool that rearranges data in neural networks) where the code doesn't check if a value called `block_size` is zero before dividing by it, which could cause a crash. An attacker could create a malicious model that sets `block_size` to zero to trigger this division-by-zero error.

TensorFlow's pooling code (the part that downsamples data in neural networks) has a bug where it doesn't check if stride values, which control how much data to skip, are zero before doing math with them. An attacker can create a special machine learning model that forces stride to be zero, causing a division by zero error (dividing by zero, which crashes programs) that could crash or be exploited.

TensorFlow, a popular machine learning platform, has a bug in TFLite (TensorFlow Lite, a lightweight version for mobile and embedded devices) where a function called `ComputeOutSize` divides by a `stride` parameter without checking if it's zero first. An attacker could create a specially crafted model that triggers this division-by-zero error, potentially crashing the application.

TensorFlow (a machine learning platform) has a vulnerability where an attacker can crash the system by triggering an integer overflow (when a number becomes too large for the system to handle) in the code that creates tensor shapes (multi-dimensional arrays). The problem occurs because the code doesn't check if dimension calculations will overflow before creating a new tensor shape.

TensorFlow's `tf.raw_ops.FusedBatchNorm` function has a vulnerability where it doesn't properly check that certain input values (scale, offset, mean, and variance) match the size of the data being processed, which can cause a heap buffer overflow (reading data beyond allocated memory boundaries) or crash the program by accessing null pointers if empty tensors are provided.