Browse All

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.

TensorFlow (an open source platform for machine learning) has a vulnerability in its `LRNGrad` function where passing an incorrectly formatted input tensor (one that is not 4-dimensional) causes the program to crash, allowing attackers to trigger a denial of service attack (making the system unavailable).

TensorFlow (an open source machine learning platform) has a bug in the `ParameterizedTruncatedNormal` function where it only accepts one data type (`int32`) for the `shape` parameter, but crashes when given the correct type (`int64`), which could allow an attacker to cause a denial of service (making the software unavailable).

TensorFlow (an open source machine learning platform) has a vulnerability where running certain save operations on data types (formats for storing numbers) that aren't supported causes the program to crash, which could be used for a denial of service attack (making a service unavailable by overwhelming it). The vulnerability affects multiple versions of TensorFlow.

TensorFlow, an open source machine learning platform, has a vulnerability in the `SparseBincount` function where invalid sparse tensor (a compressed way of storing data with mostly empty values) inputs can crash the program, potentially 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 of TensorFlow.

TensorFlow, an open source machine learning platform, has a vulnerability in its `FractionalMaxPoolGrad` function (a component that processes pooling operations) where it uses CHECK failures instead of returning errors to validate inputs. If someone sends incorrectly sized inputs to this function, they can trigger a denial of service attack (making the system crash or become unresponsive).