AI & LLM Vulnerabilities

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.

TensorFlow (an open source machine learning platform) has a vulnerability where a specific function called `FakeQuantWithMinMaxVarsPerChannel` crashes when given certain types of input data, allowing attackers to cause a denial of service attack (making the system stop working). The developers have fixed the bug in their code.

TensorFlow, an open source platform for machine learning, has a vulnerability where a function called `RaggedTensorToVariant` can crash if it receives incorrectly formatted input (tensors with ranks other than one). An attacker could use this crash to launch a denial of service attack (making the system unavailable).

A vulnerability in TensorFlow (an open source platform for machine learning) allows attackers to crash the system by sending specially formatted inputs to a specific function called `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient`, causing a denial of service attack (where a system becomes unavailable). The issue occurs when the function receives input parameters with the wrong structure (rank other than 1).

TensorFlow (an open source platform for machine learning) has a vulnerability in its MaxPool function, which crashes when given a window size array with dimensions larger than the input data, allowing attackers to cause a denial of service attack (making the system unavailable). The issue has been patched and will be fixed in upcoming versions.

TensorFlow (an open source platform for machine learning) has a vulnerability in its `tf.linalg.matrix_rank` function, which crashes when given an empty input. An attacker could exploit this crash to cause a denial of service attack (making the system unavailable by overwhelming it with requests or triggering failures).

TensorFlow, an open source platform for machine learning, has a vulnerability in its `DenseBincount` function where it doesn't properly check if the `weights` input tensor (a data structure holding numbers) has the correct shape, allowing attackers to crash the program through a denial of service attack (making a system unavailable by overwhelming it).

TensorFlow (an open source machine learning platform) has a bug where the `RaggedBincount` function crashes when given an empty input tensor called `splits`, which can be exploited to launch a denial of service attack (making a service unavailable by overwhelming it). The vulnerability affects multiple versions of the software.