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An out-of-bounds read occurs due to insufficient bounds checking. Processing a maliciously crafted file may lead to unexpected app termination.

The issue is related to an out-of-bounds read problem in the NVIDIA Triton Inference Server, which can be caused by releasing a shared memory region while it is in use. This can lead to a denial of service. The vulnerability may allow a remote attacker to cause a service disruption.

The issue is related to a command `cuobjdump` in NVIDIA CUDA Toolkit, where passing a malformed ELF file can cause a crash. This can lead to an out of bounds read in the unprivileged process memory, resulting in a limited denial of service.

The issue is related to the `cuobjdump` command in NVIDIA CUDA Toolkit, where a user can cause an out-of-bound write by passing in a malformed ELF file. This can lead to code execution or denial of service.

The issue is related to the 'cuobjdump' command in the NVIDIA CUDA Toolkit, which can be exploited by passing a malformed ELF file. This can cause a crash or produce incorrect output, potentially leading to a limited denial of service or data tampering.

TensorFlow is an open source platform for machine learning. The issue is a null point error in `QuantizedMatMulWithBiasAndDequantize` with MKL enabled. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

The function `tf.raw ops.LookupTableImportV2` cannot handle scalars in the `values` parameter and gives a Null Pointer Exception (NPE). TensorFlow is an open source platform for machine learning.

TensorFlow is an open source platform for machine learning. The issue is related to a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

TensorFlow is an open source machine learning platform. When running with XLA, `tf.raw ops.Bincount` segfaults when given a parameter `weights` that is neither the same shape as parameter `arr` nor a length-0 tensor.

The issue occurs when running TensorFlow with XLA, where `tf.raw ops.ParallelConcat` segfaults with a nullptr dereference when given a parameter `shape` with rank that is not greater than zero.

The issue is a null pointer error in RandomShuffle with XLA enabled. A fix is included in TensorFlow 2.12.0 and 2.11.1. The error can be triggered by using the `tf.raw ops.RandomShuffle` function with specific parameters, such as `value`, `seed`, and `seed2`. For example, using the parameters `{'value': 1e+20, 'seed': -4294967297, 'seed2': -2147483649}` can cause the error.

TensorFlow, an open source platform for machine learning, has an issue where an out of bounds read occurs in GRUBlockCellGrad. The function `tf.raw ops.GRUBlockCellGrad` is affected, with parameters such as `x`, `h prev`, `w ru`, `w c`, `b ru`, `b c`, `r`, `u`, `c`, and `d h` being used. This issue has been reported and a fix is included in versions 2.12.0 and 2.11.1.

The issue is related to an integer overflow in the EditDistance function of TensorFlow, which can cause a deadlock when the `hypothesis shape t` is empty. This occurs because `hypothesis shape t->NumElements() - 1` will result in an integer overflow. The vulnerability can be exploited by passing a specific set of parameters to the `tf.raw ops.EditDistance` function, including empty `hypothesis shape` and `truth shape`.

The issue is related to a floating point exception in the AudioSpectrogram function. The exception occurs when the stride is set to 0. This can be exploited by passing a `stride` value of 0 to the `AudioSpectrogram` function, which can cause a division by zero error. The vulnerability is present in versions prior to 2.12.0 and 2.11.1.

The issue arises when the stride and window size are not positive for `tf.raw ops.AvgPoolGrad`, potentially causing a floating point exception.

The issue is related to a vulnerability in cuobjdump, where a local user running the tool against a malicious binary may cause an out-of-bounds read. This may result in a limited denial of service and limited information disclosure.