Di Jin

**Name of the Vulnerable Software and Affected Versions** TensorFlow versions prior to 2.10.0 TensorFlow versions 2.9.1 and earlier TensorFlow versions 2.8.1 and earlier TensorFlow versions 2.7.2 and earlier **Description** The issue arises when `SparseBincount` is given inputs for `indices`, `values`, and `dense shape` that do not make a valid sparse tensor, resulting in a segfault that can be used to trigger a denial of service attack. **Recommendations** For versions prior to 2.10.0, update to TensorFlow 2.10.0 or later. For versions 2.9.1 and earlier, update to TensorFlow 2.9.1 or later. For versions 2.8.1 and earlier, update to TensorFlow 2.8.1 or later. For versions 2.7.2 and earlier, update to TensorFlow 2.7.2 or later. As a temporary workaround, consider avoiding the use of `SparseBincount` with invalid sparse tensor inputs until a patch is available.

**Name of the Vulnerable Software and Affected Versions** TensorFlow versions prior to 2.10.0 TensorFlow versions 2.9.1 and earlier TensorFlow versions 2.8.1 and earlier TensorFlow versions 2.7.2 and earlier **Description** The issue occurs when `Save` or `SaveSlices` is run over tensors of an unsupported `dtype`, resulting in a `CHECK` fail that can be used to trigger a denial of service attack. This can happen when using the `tf.raw ops.Save` or `tf.raw ops.SaveSlices` functions with specific parameters, such as `filename`, `tensor names`, `data`, `shapes and slices`. **Recommendations** For versions prior to 2.10.0, update to TensorFlow 2.10.0 or later. For versions 2.9.1 and earlier, update to TensorFlow 2.9.1 or later. For versions 2.8.1 and earlier, update to TensorFlow 2.8.1 or later. For versions 2.7.2 and earlier, update to TensorFlow 2.7.2 or later. As a temporary workaround, consider avoiding the use of `Save` or `SaveSlices` functions with unsupported `dtype` until a patch is available.

**Name of the Vulnerable Software and Affected Versions** TensorFlow versions prior to 2.10.0 TensorFlow versions 2.9.1 and earlier TensorFlow versions 2.8.1 and earlier TensorFlow versions 2.7.2 and earlier **Description** The issue arises from `ParameterizedTruncatedNormal` assuming `shape` is of type `int32`, but a valid `shape` of type `int64` results in a mismatched type `CHECK` fail that can be used to trigger a denial of service attack. This can be exploited by providing a `shape` variable of type `int64`. The estimated number of potentially affected devices is not specified. **Recommendations** For versions prior to 2.10.0, update to TensorFlow 2.10.0 or later. For versions 2.9.1 and earlier, update to TensorFlow 2.9.1 or later. For versions 2.8.1 and earlier, update to TensorFlow 2.8.1 or later. For versions 2.7.2 and earlier, update to TensorFlow 2.7.2 or later. As a temporary workaround, consider avoiding the use of `ParameterizedTruncatedNormal` with `shape` variables of type `int64` until a patch is available.

**Name of the Vulnerable Software and Affected Versions** TensorFlow versions prior to 2.10.0 TensorFlow versions 2.9.1 and earlier TensorFlow versions 2.8.1 and earlier TensorFlow versions 2.7.2 and earlier **Description** The issue arises when the `LRNGrad` function is given an `output image` input tensor that is not 4-D, resulting in a `CHECK` fail that can be used to trigger a denial of service attack. This can be exploited by providing a malformed input tensor to the `tf.raw ops.LRNGrad` function, specifically the `output image` parameter. The estimated number of potentially affected devices worldwide is not provided. **Recommendations** For versions prior to 2.10.0, update to TensorFlow 2.10.0 or later. For versions 2.9.1 and earlier, update to TensorFlow 2.9.1 or later. For versions 2.8.1 and earlier, update to TensorFlow 2.8.1 or later. For versions 2.7.2 and earlier, update to TensorFlow 2.7.2 or later. As a temporary workaround, consider validating the dimensions of the `output image` tensor before passing it to the `LRNGrad` function to prevent the denial of service attack.

**Name of the Vulnerable Software and Affected Versions** TensorFlow versions prior to 2.10.0 TensorFlow versions 2.9.1, 2.8.1, and 2.7.2 **Description** The issue occurs when `RaggedBincount` is given an empty input tensor `splits`, resulting in a segfault that can be used to trigger a denial of service attack. There are no known workarounds for this issue. **Recommendations** For TensorFlow versions prior to 2.10.0, update to version 2.10.0 or later. For TensorFlow versions 2.9.1, 2.8.1, and 2.7.2, update to the respective patched versions. As a temporary workaround, consider avoiding the use of `RaggedBincount` with empty input tensors until a patch is available.

**Name of the Vulnerable Software and Affected Versions** TensorFlow versions prior to 2.10.0 TensorFlow versions 2.9.0 through 2.9.0 TensorFlow versions 2.8.0 through 2.8.0 TensorFlow versions 2.7.0 through 2.7.1 **Description** The `DenseBincount` function in TensorFlow assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack. **Recommendations** For TensorFlow versions prior to 2.10.0, update to TensorFlow 2.10.0 or later. For TensorFlow versions 2.9.0, update to TensorFlow 2.9.1 or later. For TensorFlow versions 2.8.0, update to TensorFlow 2.8.1 or later. For TensorFlow versions 2.7.0 through 2.7.1, update to TensorFlow 2.7.2 or later.