AI Sec Watch

A vulnerability in TensorFlow (an open source machine learning framework) exists in the Grappler optimizer, which can be exploited to cause a denial of service (making a system unavailable by overloading it) by modifying a SavedModel file so that a function called IsSimplifiableReshape triggers CHECK failures (unexpected error conditions that crash the program).

TensorFlow, an open-source machine learning framework, has a vulnerability in its shape inference process where it can allocate a large vector based on user-controlled input, potentially causing uncontrolled resource consumption (using excessive memory or CPU). This happens because the system doesn't properly validate the size of data requested by users.

TensorFlow (an open source machine learning framework) has a vulnerability in its Grappler optimizer (a tool that improves how machine learning models run) that allows attackers to cause a denial of service (making the system stop working) by modifying a SavedModel (a saved machine learning model) in a way that triggers crashes. This vulnerability affects multiple versions of TensorFlow.

TensorFlow (an open-source machine learning framework) has a memory leak bug in a function called `ImmutableExecutorState::Initialize`. When a graph node (a processing unit in a machine learning model) is invalid, the software sets a pointer (a reference to a location in memory) to null without freeing the memory it previously pointed to, causing that memory to be wasted and unavailable for other tasks.

TensorFlow, an open source machine learning framework, has a vulnerability in the `GetInitOp` function that can crash the software through a null pointer dereference (accessing memory that doesn't exist). The vulnerability affects multiple versions of TensorFlow.

TensorFlow (an open-source machine learning framework) has a vulnerability in its `OpLevelCostEstimator::CalculateOutputSize` function where an integer overflow (when a calculation produces a number too large for the system to handle) can occur if an attacker creates an operation with tensors (multi-dimensional arrays of numbers) containing enough elements. The vulnerability can be triggered either by using many dimensions or by making individual dimensions large enough to cause the overflow.

TensorFlow, an open-source machine learning framework, has a vulnerability in its `OpLevelCostEstimator::CalculateTensorSize` function that can be exploited through integer overflow (a type of bug where numbers become too large for the program to handle correctly). An attacker could trigger this by creating an operation with a tensor (a multi-dimensional array of data) containing an extremely large number of elements.

TensorFlow, an open-source machine learning framework, has a typo in its `SpecializeType` code that causes a heap OOB (out-of-bounds, where the program tries to read or write memory outside the area it's allowed to access) read/write vulnerability. Due to the typo, a variable called `arg` uses the wrong loop index, which allows code to read and modify data outside the intended memory bounds.

TensorFlow's `AssignOp` (a copy operation in machine learning code) has a bug where it can copy uninitialized data (memory with random or leftover values) to a new tensor, causing unpredictable behavior. The code only checks that the destination is ready, but not the source, leaving room for uninitialized data to be used.