ghsa

### Impact The implementation of [`tf.ragged.constant`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/python/ops/ragged/ragged_factory_ops.py#L146-L239) does not fully validate the input arguments. This results in a denial of service by consuming all available memory: ```python import tensorflow as tf tf.ragged.constant(pylist=[],ragged_rank=8968073515812833920) ``` ### Patches We have patched the issue in GitHub commit [bd4d5583ff9c8df26d47a23e508208844297310e](https://github.com/tensorflow/tensorflow/commit/bd4d5583ff9c8df26d47a23e508208844297310e). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how...

### Impact The implementation of [`tf.raw_ops.QuantizedConv2D`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantized_conv_ops.cc) does not fully validate the input arguments: ```python import tensorflow as tf input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8) filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8) # bad args min_input = tf.constant([], shape=[0], dtype=tf.float32) max_input = tf.constant(0, shape=[], dtype=tf.float32) min_filter = tf.constant(0, shape=[], dtype=tf.float32) max_filter = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.QuantizedConv2D( input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=[1, 1, 1, 1], padding="SAME") ``` In this case, references get bound to `nullptr` for each argument that is empty (in the example, all arguments in the `bad args` section). ### Patches We have...

### Impact The implementation of [`tf.raw_ops.LSTMBlockCell`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/rnn/lstm_ops.cc) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf tf.raw_ops.LSTMBlockCell( x=tf.constant(0.837607, shape=[28,29], dtype=tf.float32), cs_prev=tf.constant(0, shape=[28,17], dtype=tf.float32), h_prev=tf.constant(0.592631638, shape=[28,17], dtype=tf.float32), w=tf.constant(0.887386262, shape=[46,68], dtype=tf.float32), wci=tf.constant(0, shape=[], dtype=tf.float32), wcf=tf.constant(0, shape=[17], dtype=tf.float32), wco=tf.constant(0.592631638, shape=[28,17], dtype=tf.float32), b=tf.constant(0.75259006, shape=[68], dtype=tf.float32), forget_bias=1, cell_clip=0, use_peephole=False) ``` The code does not validate the ranks of any of the arguments to this API call...

### Impact The implementation of [`tf.raw_ops.LoadAndRemapMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/load_and_remap_matrix_op.cc#L70-L98) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf ckpt_path = tf.constant( "/tmp/warm_starting_util_test5kl2a3pc/tmpph76tep2/model-0", shape=[], dtype=tf.string) old_tensor_name = tf.constant( "/tmp/warm_starting_util_test5kl2a3pc/tmpph76tep2/model-0", shape=[], dtype=tf.string) row_remapping = tf.constant(0, shape=[], dtype=tf.int64) col_remapping = tf.constant(3, shape=[3], dtype=tf.int64) initializing_values = tf.constant([], shape=[0, 1], dtype=tf.float32) tf.raw_ops.LoadAndRemapMatrix( ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, row_remapping=row_remapping, col_remapping=col_remapping, initializing_values=initializing_va...

### Impact The implementation of [`tf.raw_ops.SparseTensorToCSRSparseMatrix`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/sparse/sparse_tensor_to_csr_sparse_matrix_op.cc#L65-L119) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf indices = tf.constant(53, shape=[3], dtype=tf.int64) values = tf.constant(0.554979503, shape=[218650], dtype=tf.float32) dense_shape = tf.constant(53, shape=[3], dtype=tf.int64) tf.raw_ops.SparseTensorToCSRSparseMatrix( indices=indices, values=values, dense_shape=dense_shape) ``` The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this: ```cc const Tensor& indices = ctx->input(0); const Tensor& values = ctx->input(1); const Tensor& dense_shape = ctx->input(...

### Impact The implementation of [`tf.raw_ops.UnsortedSegmentJoin`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L95) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf tf.raw_ops.UnsortedSegmentJoin( inputs=tf.constant("this", shape=[12], dtype=tf.string), segment_ids=tf.constant(0, shape=[12], dtype=tf.int64), num_segments=tf.constant(0, shape=[12], dtype=tf.int64)) ``` The code assumes `num_segments` is a scalar but there is no validation for this before accessing its value: ```cc const Tensor& num_segments_tensor = context->input(2); OP_REQUIRES(context, num_segments_tensor.NumElements() != 0, errors::InvalidArgument("Number of segments cannot be empty.")); auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()(); ``` ### Patches...

### Impact The implementation of [`tf.raw_ops.Conv3DBackpropFilterV2`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/conv_grad_ops_3d.cc) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf tf.raw_ops.Conv3DBackpropFilterV2( input=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16), filter_sizes=tf.constant(0, shape=[], dtype=tf.int32), out_backprop=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16), strides=[1, 1, 1, 1, 1], padding="VALID", data_format="NDHWC", dilations=[1, 1, 1, 1, 1]) ``` The code does not validate that the `filter_sizes` argument is a vector. ### Patches We have patched the issue in GitHub commit [174c5096f303d5be7ed2ca2662b08371bff4ab88](https://github.com/tensorflow/tensorflow/commit/174c5096f303d5be7ed2ca2662b08371bff4ab88). The fix will ...

### Impact The implementation of [`tf.raw_ops.StagePeek`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/stage_op.cc#L261) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf index = tf.constant([], shape=[0], dtype=tf.int32) tf.raw_ops.StagePeek(index=index, dtypes=[tf.int32]) ``` The code assumes `index` is a scalar but there is no validation for this before accessing its value: ```cc std::size_t index = ctx->input(0).scalar<int>()(); ``` ### Patches We have patched the issue in GitHub commit [cebe3c45d76357d201c65bdbbf0dbe6e8a63bbdb](https://github.com/tensorflow/tensorflow/commit/cebe3c45d76357d201c65bdbbf0dbe6e8a63bbdb). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and...

### Impact The implementation of [`tf.raw_ops.DeleteSessionTensor`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/session_ops.cc#L128-L144) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf handle = tf.constant("[]", shape=[0], dtype=tf.string) tf.raw_ops.DeleteSessionTensor(handle=handle) ``` The code assumes `handle` is a scalar but there is no validation for this: ```cc const Tensor& handle = ctx->input(0); const string& name = handle.scalar<tstring>()(); ``` ### Patches We have patched the issue in GitHub commit [cff267650c6a1b266e4b4500f69fbc49cdd773c5](https://github.com/tensorflow/tensorflow/commit/cff267650c6a1b266e4b4500f69fbc49cdd773c5). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4...

### Impact The implementation of [`tf.raw_ops.QuantizeAndDequantizeV4Grad`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L148-L226) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf tf.raw_ops.QuantizeAndDequantizeV4Grad( gradients=tf.constant(1, shape=[2,2], dtype=tf.float64), input=tf.constant(1, shape=[2,2], dtype=tf.float64), input_min=tf.constant([], shape=[0], dtype=tf.float64), input_max=tf.constant(-10, shape=[], dtype=tf.float64), axis=-1) ``` The code assumes `input_min` and `input_max` are scalars but there is no validation for this. ### Patches We have patched the issue in GitHub commit [098e7762d909bac47ce1dbabe6dfd06294cb9d58](https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58). The fix will be included ...