CVE-2021-29530: NULL Pointer Dereference (CWE-476)
value: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
CWE description: The product dereferences a pointer that it expects to be valid but is NULL.
CVSS v3 base score: 7.8
Published: 2021-05-14T20:15Z
Repository: https://github.com/tensorflow/tensorflow
Language: C++
Files changed: tensorflow/core/kernels/sparse/sparse_cholesky_op.cc
Commit date: 2021-04-20 14:49:18 -0700
Commit message: Remove `OP_REQUIRES` call from helper function.

Since `OP_REQUIRES` macro expands to a `return;` (among other), calling it in a helper function only ends the helper function's execution earlier, but the kernel will still run from start to end. Thus, all the expected validations are actually broken/useless as the code ploughs through the next crash anyway.

PiperOrigin-RevId: 369524386
Change-Id: I54f6cf9328445675ccc392e661b04336b229c9da

Diff: diff --git a/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc b/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc
index 9a939276f0b6c..47ab252317de5 100644
--- a/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc
+++ b/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc
@@ -15,10 +15,12 @@ limitations under the License.
 
 #include <atomic>
 #include <numeric>
 #include <vector>
 
+#include "tensorflow/core/framework/op_requires.h"
+
 #define EIGEN_USE_THREADS
 
 #include "third_party/eigen3/Eigen/Core"
 #include "third_party/eigen3/Eigen/SparseCholesky"
 #include "third_party/eigen3/Eigen/SparseCore"
@@ -80,12 +82,12 @@ class CSRSparseCholeskyCPUOp : public OpKernel {
     OP_REQUIRES_OK(ctx, ExtractVariantFromInput(ctx, 0, &input_matrix));
     const Tensor& input_permutation_indices = ctx->input(1);
 
     int64 num_rows;
     int batch_size;
-    ValidateInputs(ctx, *input_matrix, input_permutation_indices, &batch_size,
-                   &num_rows);
+    OP_REQUIRES_OK(ctx, ValidateInputs(*input_matrix, input_permutation_indices,
+                                       &batch_size, &num_rows));
 
     // Allocate batch pointers.
     Tensor batch_ptr(cpu_allocator(), DT_INT32, TensorShape({batch_size + 1}));
     auto batch_ptr_vec = batch_ptr.vec<int32>();
     batch_ptr_vec(0) = 0;
@@ -224,53 +226,52 @@ class CSRSparseCholeskyCPUOp : public OpKernel {
     output_csr_matrix_tensor->scalar<Variant>()() =
         std::move(output_csr_matrix);
   }
 
  private:
-  void ValidateInputs(OpKernelContext* ctx,
-                      const CSRSparseMatrix& sparse_matrix,
-                      const Tensor& permutation_indices, int* batch_size,
-                      int64* num_rows) {
-    OP_REQUIRES(ctx, sparse_matrix.dtype() == DataTypeToEnum<T>::value,
-                errors::InvalidArgument(
-                    "Asked for a CSRSparseMatrix of type ",
-                    DataTypeString(DataTypeToEnum<T>::value),
-                    " but saw dtype: ", DataTypeString(sparse_matrix.dtype())));
+  Status ValidateInputs(const CSRSparseMatrix& sparse_matrix,
+                        const Tensor& permutation_indices, int* batch_size,
+                        int64* num_rows) {
+    if (sparse_matrix.dtype() != DataTypeToEnum<T>::value)
+      return errors::InvalidArgument(
+          "Asked for a CSRSparseMatrix of type ",
+          DataTypeString(DataTypeToEnum<T>::value),
+          " but saw dtype: ", DataTypeString(sparse_matrix.dtype()));
 
     const Tensor& dense_shape = sparse_matrix.dense_shape();
     const int rank = dense_shape.dim_size(0);
-    OP_REQUIRES(ctx, rank == 2 || rank == 3,
-                errors::InvalidArgument("sparse matrix must have rank 2 or 3; ",
-                                        "but dense_shape has size ", rank));
+    if (rank < 2 || rank > 3)
+      return errors::InvalidArgument("sparse matrix must have rank 2 or 3; ",
+                                     "but dense_shape has size ", rank);
     const int row_dim = (rank == 2) ? 0 : 1;
     auto dense_shape_vec = dense_shape.vec<int64>();
     *num_rows = dense_shape_vec(row_dim);
     const int64 num_cols = dense_shape_vec(row_dim + 1);
-    OP_REQUIRES(ctx, *num_rows == num_cols,
-                errors::InvalidArgument("sparse matrix must be square; got: ",
-                                        *num_rows, " != ", num_cols));
+    if (*num_rows != num_cols)
+      return errors::InvalidArgument(
+          "sparse matrix must be square; got: ", *num_rows, " != ", num_cols);
     const TensorShape& perm_shape = permutation_indices.shape();
-    OP_REQUIRES(
-        ctx, perm_shape.dims() + 1 == rank,
-        errors::InvalidArgument(
-            "sparse matrix must have the same rank as permutation; got: ", rank,
-            " != ", perm_shape.dims(), " + 1."));
-    OP_REQUIRES(
-        ctx, perm_shape.dim_size(rank - 2) == *num_rows,
-        errors::InvalidArgument(
-            "permutation must have the same number of elements in each batch "
-            "as the number of rows in sparse matrix; got: ",
-            perm_shape.dim_size(rank - 2), " != ", *num_rows));
+    if (perm_shape.dims() + 1 != rank)
+      return errors::InvalidArgument(
+          "sparse matrix must have the same rank as permutation; got: ", rank,
+          " != ", perm_shape.dims(), " + 1.");
+    if (perm_shape.dim_size(rank - 2) != *num_rows)
+      return errors::InvalidArgument(
+          "permutation must have the same number of elements in each batch "
+          "as the number of rows in sparse matrix; got: ",
+          perm_shape.dim_size(rank - 2), " != ", *num_rows);
 
     *batch_size = sparse_matrix.batch_size();
     if (*batch_size > 1) {
-      OP_REQUIRES(
-          ctx, perm_shape.dim_size(0) == *batch_size,
-          errors::InvalidArgument("permutation must have the same batch size "
-                                  "as sparse matrix; got: ",
-                                  perm_shape.dim_size(0), " != ", *batch_size));
+      if (perm_shape.dim_size(0) != *batch_size)
+        return errors::InvalidArgument(
+            "permutation must have the same batch size "
+            "as sparse matrix; got: ",
+            perm_shape.dim_size(0), " != ", *batch_size);
     }
+
+    return Status::OK();
   }
 };
 
 #define REGISTER_CPU(T)                                      \
   REGISTER_KERNEL_BUILDER(Name("SparseMatrixSparseCholesky") \

Vulnerable code:     ValidateInputs(ctx, *input_matrix, input_permutation_indices, &batch_size,
                   &num_rows);
  void ValidateInputs(OpKernelContext* ctx,
                      const CSRSparseMatrix& sparse_matrix,
                      const Tensor& permutation_indices, int* batch_size,
                      int64* num_rows) {
    OP_REQUIRES(ctx, sparse_matrix.dtype() == DataTypeToEnum<T>::value,
                errors::InvalidArgument(
                    "Asked for a CSRSparseMatrix of type ",
                    DataTypeString(DataTypeToEnum<T>::value),
                    " but saw dtype: ", DataTypeString(sparse_matrix.dtype())));
    OP_REQUIRES(ctx, rank == 2 || rank == 3,
                errors::InvalidArgument("sparse matrix must have rank 2 or 3; ",
                                        "but dense_shape has size ", rank));
    OP_REQUIRES(ctx, *num_rows == num_cols,
                errors::InvalidArgument("sparse matrix must be square; got: ",
                                        *num_rows, " != ", num_cols));
    OP_REQUIRES(
        ctx, perm_shape.dims() + 1 == rank,
        errors::InvalidArgument(
            "sparse matrix must have the same rank as permutation; got: ", rank,
            " != ", perm_shape.dims(), " + 1."));
    OP_REQUIRES(
        ctx, perm_shape.dim_size(rank - 2) == *num_rows,
        errors::InvalidArgument(
            "permutation must have the same number of elements in each batch "
            "as the number of rows in sparse matrix; got: ",
            perm_shape.dim_size(rank - 2), " != ", *num_rows));
      OP_REQUIRES(
          ctx, perm_shape.dim_size(0) == *batch_size,
          errors::InvalidArgument("permutation must have the same batch size "
                                  "as sparse matrix; got: ",
                                  perm_shape.dim_size(0), " != ", *batch_size));
Fixed code: #include "tensorflow/core/framework/op_requires.h"

    OP_REQUIRES_OK(ctx, ValidateInputs(*input_matrix, input_permutation_indices,
                                       &batch_size, &num_rows));
  Status ValidateInputs(const CSRSparseMatrix& sparse_matrix,
                        const Tensor& permutation_indices, int* batch_size,
                        int64* num_rows) {
    if (sparse_matrix.dtype() != DataTypeToEnum<T>::value)
      return errors::InvalidArgument(
          "Asked for a CSRSparseMatrix of type ",
          DataTypeString(DataTypeToEnum<T>::value),
          " but saw dtype: ", DataTypeString(sparse_matrix.dtype()));
    if (rank < 2 || rank > 3)
      return errors::InvalidArgument("sparse matrix must have rank 2 or 3; ",
                                     "but dense_shape has size ", rank);
    if (*num_rows != num_cols)
      return errors::InvalidArgument(
          "sparse matrix must be square; got: ", *num_rows, " != ", num_cols);
    if (perm_shape.dims() + 1 != rank)
      return errors::InvalidArgument(
          "sparse matrix must have the same rank as permutation; got: ", rank,
          " != ", perm_shape.dims(), " + 1.");
    if (perm_shape.dim_size(rank - 2) != *num_rows)
      return errors::InvalidArgument(
          "permutation must have the same number of elements in each batch "
          "as the number of rows in sparse matrix; got: ",
          perm_shape.dim_size(rank - 2), " != ", *num_rows);
      if (perm_shape.dim_size(0) != *batch_size)
        return errors::InvalidArgument(
            "permutation must have the same batch size "
            "as sparse matrix; got: ",
            perm_shape.dim_size(0), " != ", *batch_size);

    return Status::OK();