CVE-2021-37665: Improper Input Validation (CWE-20)
value: TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
CWE description: The product receives input or data, but it does
        not validate or incorrectly validates that the input has the
        properties that are required to process the data safely and
        correctly.
CVSS v3 base score: 7.8
Published: 2021-08-12T23:15Z
Repository: https://github.com/tensorflow/tensorflow
Language: C++
Files changed: tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc
Commit date: 2021-07-29 16:35:05 -0700
Commit message: Add more validation to `RequantizationRangePerChannel`.

PiperOrigin-RevId: 387693946
Change-Id: Ife8dcbdb021bec4787eef6a4361dd08f17c14bd6

Diff: diff --git a/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc b/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc
index 24dabb07ca067..a38df2450d194 100644
--- a/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc
+++ b/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc
@@ -55,10 +55,24 @@ class MklRequantizationRangePerChannelOp : public OpKernel {
                                 depth, " was ", input_min.dim_size(0)));
     OP_REQUIRES(
         ctx, input_max.dim_size(0) == depth,
         errors::InvalidArgument("input_max has incorrect size, expected ",
                                 depth, " was ", input_max.dim_size(0)));
+    OP_REQUIRES(
+        ctx, input_min.NumElements() == depth,
+        errors::InvalidArgument("input_min must have the same number of "
+                                "elements as input_max, got ",
+                                input_min.NumElements(), " and ", depth));
+    OP_REQUIRES(ctx, input.NumElements() > 0,
+                errors::InvalidArgument("input must not be empty"));
+    OP_REQUIRES(ctx, input.dims() == 4,
+                errors::InvalidArgument("input must be in NHWC format"));
+    OP_REQUIRES(
+        ctx, input.dim_size(3) == depth,
+        errors::InvalidArgument(
+            "input must have same number of channels as length of input_min: ",
+            input.dim_size(3), " vs ", depth));
 
     const float* input_min_data = input_min.flat<float>().data();
     const float* input_max_data = input_max.flat<float>().data();
     std::vector<float> ranges(depth);
     bool is_non_negative = true;

Fixed code:     OP_REQUIRES(
        ctx, input_min.NumElements() == depth,
        errors::InvalidArgument("input_min must have the same number of "
                                "elements as input_max, got ",
                                input_min.NumElements(), " and ", depth));
    OP_REQUIRES(ctx, input.NumElements() > 0,
                errors::InvalidArgument("input must not be empty"));
    OP_REQUIRES(ctx, input.dims() == 4,
                errors::InvalidArgument("input must be in NHWC format"));
    OP_REQUIRES(
        ctx, input.dim_size(3) == depth,
        errors::InvalidArgument(
            "input must have same number of channels as length of input_min: ",
            input.dim_size(3), " vs ", depth));