Commit message: Fix segmentation fault in shape inference logic.

When running shape functions, some functions (such as `MutableHashTableShape`)
produce extra output information in the form of a `ShapeAndType` struct.  The
shapes embedded in this struct are owned by an inference context that is
cleaned up almost immediately; if the upstream code attempts to access this
shape information, it can trigger a segfault.

`ShapeRefiner` is mitigating this for normal output shapes by cloning them
(and thus putting the newly created shape under ownership of an inference
context that will not die), but we were not doing the same for shapes and
types.  This commit fixes that by doing similar logic on output shapes and
types.

PiperOrigin-RevId: 384761124
Change-Id: I07c0c42d29dfbb55bfa13ec1f09ef825fb0a1a1d
CVE: CVE-2021-37690
CWE: CWE-369
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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.

Project: tensorflow

Status ShapeRefiner::InferShapesForFunctionSubNode(
    const Node* node, InferenceContext* outer_context) {
  TF_RETURN_IF_ERROR(AddNodeInternal(node, outer_context));
  InferenceContext* node_context = CHECK_NOTNULL(GetContext(node));

  if (StringPiece(node->type_string()) == kArgOp) {
    // Handle special node: function input.
    // Shapes for these nodes are provided in the outer inference
    // context.

    int index;
    TF_RETURN_IF_ERROR(GetNodeAttr(AttrSlice(node->def()), "index", &index));

    if (index < 0 || outer_context->num_inputs() <= index) {
      return errors::Internal(
          "Function instantiation included invalid input index: ", index,
          " not in [0, ", outer_context->num_inputs(), ").");
    }

    // TODO(b/134547156): TEMPORARY WORKAROUND. If input shape handle is not set
    // in outer context, set _Arg node output shape to unknown.
    if (outer_context->input(index).SameHandle(ShapeHandle())) {
      VLOG(1) << "Function instantiation has undefined input shape at "
              << "index: " << index << " in the outer inference context.";
      node_context->set_output(0, node_context->UnknownShape());
    } else {
      node_context->set_output(0, outer_context->input(index));
    }

    auto* resource = outer_context->input_handle_shapes_and_types(index);
    if (resource) {
      node_context->set_output_handle_shapes_and_types(0, *resource);
    }
  } else if (StringPiece(node->type_string()) == kRetvalOp) {
    // Handle special node: function output.
    // Shapes inferred for these nodes go into the outer inference
    // context.

    int index;
    TF_RETURN_IF_ERROR(GetNodeAttr(AttrSlice(node->def()), "index", &index));

    if (index < 0 || outer_context->num_outputs() <= index) {
      return errors::Internal(
          "Function instantiation included invalid output index: ", index,
          " not in [0, ", outer_context->num_outputs(), ").");
    }

    // outer_context outlives node_context, therefore we need to create
    // a new shape handle owned by outer_context instead.
    ShapeHandle handle;
    TensorShapeProto proto;
    node_context->ShapeHandleToProto(node_context->input(0), &proto);
    TF_RETURN_IF_ERROR(outer_context->MakeShapeFromShapeProto(proto, &handle));
    outer_context->set_output(index, handle);

    auto* resource = node_context->input_handle_shapes_and_types(0);
    if (resource) {
      outer_context->set_output_handle_shapes_and_types(index, *resource);
    }
  }

  return Status::OK();
}

Project: tensorflow

Status ShapeRefiner::InferShapesForFunctionSubNode(
    const Node* node, InferenceContext* outer_context) {
  TF_RETURN_IF_ERROR(AddNodeInternal(node, outer_context));
  InferenceContext* node_context = CHECK_NOTNULL(GetContext(node));

  if (StringPiece(node->type_string()) == kArgOp) {
    // Handle special node: function input.
    // Shapes for these nodes are provided in the outer inference
    // context.

    int index;
    TF_RETURN_IF_ERROR(GetNodeAttr(AttrSlice(node->def()), "index", &index));

    if (index < 0 || outer_context->num_inputs() <= index) {
      return errors::Internal(
          "Function instantiation included invalid input index: ", index,
          " not in [0, ", outer_context->num_inputs(), ").");
    }

    // TODO(b/134547156): TEMPORARY WORKAROUND. If input shape handle is not set
    // in outer context, set _Arg node output shape to unknown.
    if (outer_context->input(index).SameHandle(ShapeHandle())) {
      VLOG(1) << "Function instantiation has undefined input shape at "
              << "index: " << index << " in the outer inference context.";
      node_context->set_output(0, node_context->UnknownShape());
    } else {
      node_context->set_output(0, outer_context->input(index));
    }

    auto* resource = outer_context->input_handle_shapes_and_types(index);
    if (resource) {
      node_context->set_output_handle_shapes_and_types(0, *resource);
    }
  } else if (StringPiece(node->type_string()) == kRetvalOp) {
    // Handle special node: function output.
    // Shapes inferred for these nodes go into the outer inference
    // context.

    int index;
    TF_RETURN_IF_ERROR(GetNodeAttr(AttrSlice(node->def()), "index", &index));

    if (index < 0 || outer_context->num_outputs() <= index) {
      return errors::Internal(
          "Function instantiation included invalid output index: ", index,
          " not in [0, ", outer_context->num_outputs(), ").");
    }

    // outer_context outlives node_context, therefore we need to create
    // a new shape handle owned by outer_context instead.
    ShapeHandle handle;
    TensorShapeProto proto;
    node_context->ShapeHandleToProto(node_context->input(0), &proto);
    TF_RETURN_IF_ERROR(outer_context->MakeShapeFromShapeProto(proto, &handle));
    outer_context->set_output(index, handle);

    const std::vector<ShapeAndType>* resource =
        node_context->input_handle_shapes_and_types(0);
    if (resource) {
      // `ShapesAndType`s contain `ShapeHandle`s.  These `ShapeHandle`s point
      // to `Shape`s that are owned by a different inference context too.  We
      // need to copy them to the outer context to prevent them from being
      // destroyed before they are used.
      std::vector<ShapeAndType> copied_shapes_and_types;
      for (auto& shape_and_type : *resource) {
        ShapeHandle handle;
        TensorShapeProto proto;
        node_context->ShapeHandleToProto(shape_and_type.shape, &proto);
        TF_RETURN_IF_ERROR(
            outer_context->MakeShapeFromShapeProto(proto, &handle));
        copied_shapes_and_types.push_back(
            ShapeAndType(handle, shape_and_type.dtype, shape_and_type.type));
      }

      outer_context->set_output_handle_shapes_and_types(
          index, copied_shapes_and_types);
    }
  }

  return Status::OK();
}