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TensorFlow (an open source machine learning platform) crashes when a specific internal function receives null type list attributes (empty or missing type information). The developers have fixed the bug and will release the patch in upcoming versions of the software.

TensorFlow (an open source platform for machine learning) crashes when a component called mlir::tfg::GraphDefImporter::ConvertNodeDef tries to convert NodeDefs (data structures that define operations) without an operation name. This is a crash vulnerability that could cause the software to stop working unexpectedly.

TensorFlow (an open source platform for machine learning) crashes when a specific internal function called `mlir::tfg::ConvertGenericFunctionToFunctionDef` receives empty function attributes (data describing how a function should behave). This is a reachable assertion vulnerability, meaning the program encounters an unexpected condition it cannot handle.

TensorFlow, an open source machine learning platform, has a bug where a specific function crashes with a null dereference (trying to use a memory address that doesn't exist) when given empty function attributes. The issue affects multiple versions of TensorFlow and has no known workarounds.

TensorFlow, an open source platform for machine learning, has a vulnerability in its `tf.quantization.fake_quant_with_min_max_vars_gradient` function where nonscalar (multi-dimensional) input values for `min` or `max` parameters cause a CHECK fail, which is a crash that could enable a denial of service attack (disrupting service availability). The vulnerability affects multiple supported versions of TensorFlow.

TensorFlow (an open source machine learning platform) has a bug in its `tf.random.gamma` function where large input values can cause a denial of service attack (making the system crash or stop responding). The developers have fixed the issue and will release it in TensorFlow 2.10.0, along with updates to older supported versions.

TensorFlow (an open source machine learning platform) has a vulnerability in its `RandomPoissonV2` function where large input values can cause a CHECK fail (a safety check that stops execution), allowing attackers to trigger a denial of service attack (making the system unavailable). The vulnerability affects multiple versions of TensorFlow.

TensorFlow (an open source machine learning platform) has a vulnerability where the `Unbatch` operation crashes when it receives a nonscalar input `id` (a variable with multiple dimensions rather than a single value), which can be exploited to cause a denial of service attack (making a system unavailable by overwhelming it).

TensorFlow (an open-source machine learning platform) has a vulnerability in its `DrawBoundingBoxes` function where receiving input boxes that aren't float data types causes a CHECK fail, which can be exploited to disable the system through a denial of service attack (overwhelming it with requests). The vulnerability affects multiple versions of TensorFlow.

TensorFlow, an open-source machine learning platform, has a vulnerability where a specific internal function crashes when it receives empty function attributes, causing a null dereference (an error where the software tries to use a memory location that doesn't exist). This bug affects multiple versions of TensorFlow and has no known workarounds.

TensorFlow (an open source platform for machine learning) has a vulnerability where a function called `Conv2DBackpropInput` crashes when it receives empty input arrays, allowing attackers to cause a denial of service attack (making the system unavailable). The issue affects both CPU and GPU processing and has been patched in the codebase.

TensorFlow, an open source platform for machine learning, has a vulnerability in its `EmptyTensorList` function that crashes when given certain inputs, allowing attackers to trigger a denial of service attack (making a service unavailable by overwhelming it). The bug occurs when the function receives an `element_shape` input with more than one dimension.

TensorFlow, an open source machine learning platform, has a vulnerability in its `tf.sparse.cross` function where passing a non-scalar `separator` input (a parameter that isn't a single value) causes a CHECK fail, which can crash the program in a denial of service attack (making a system unavailable by overwhelming it). The flaw affects multiple versions of TensorFlow.

TensorFlow, an open source machine learning platform, has a bug in its `Conv2D` function (a tool for processing image data) where empty input combined with certain filter and padding settings causes division-by-zero errors. This vulnerability allows attackers to crash the system in a denial of service attack (temporarily making a service unavailable by overwhelming or breaking it).

TensorFlow (an open source machine learning platform) has a vulnerability in its `AudioSummaryV2` function where passing a `sample_rate` input with multiple elements causes a CHECK failure, which can be exploited to trigger a denial of service attack (making the system unavailable by overloading it).

TensorFlow (an open source platform for machine learning) has a vulnerability where a function called `CollectiveGather` crashes when it receives a scalar input (a single number rather than a list of numbers), allowing attackers to cause a denial of service attack (making the system unavailable). The issue has been fixed and will be released in upcoming versions of TensorFlow.

TensorFlow has a vulnerability where the `SetSize` function crashes when it receives an input called `set_shape` that is not a 1D tensor (a one-dimensional array of data). An attacker can exploit this crash to launch a denial of service attack (making the system unavailable to legitimate users).

TensorFlow (an open source machine learning platform) has a bug in the `TensorListFromTensor` function where certain inputs cause a CHECK failure that can be exploited to crash the system. This vulnerability affects multiple versions of TensorFlow and has no known workarounds.

TensorFlow, an open-source machine learning platform, has a vulnerability where two functions (`TensorListScatter` and `TensorListScatterV2`) crash when given certain types of input, allowing attackers to cause a denial of service attack (making the system unavailable). The issue has been fixed and will be released in upcoming versions.

TensorFlow, an open source platform for machine learning, has a vulnerability in its `QuantizeAndDequantizeV3` function where passing a nonscalar `num_bits` input tensor (a multi-dimensional array instead of a single value) causes the program to crash, which can be exploited for a denial of service attack (making a service unavailable by overwhelming or crashing it). The issue affects multiple TensorFlow versions.