CVE-2020-15203 (C++, published 2020-09-25T19:15Z)
Description: value: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Severity: MEDIUM (CVSS v3 base score: 7.5)
Weakness: CWE-134 \u2014 Use of Externally-Controlled Format String
The product uses a function that accepts a format string as an argument, but the format string originates from an external source.
Repository: https://github.com/tensorflow/tensorflow
Files changed:
tensorflow/core/kernels/BUILD
tensorflow/core/kernels/as_string_op.cc
tensorflow/core/kernels/as_string_op_test.cc
Commit message: Prevent format string vulnerability in `tf.strings.as_string`.

The `printf` format specifier only allows `#`, `0`, `-`, `+` and space as flag characters. Others are interpreted as width/precision/length modifier or conversion specifiers. If a character does not fit into any of these sets `printf` just displays it.

Also add a test suite for `tf.strings.as_string`. Also fix the issue where the flag character was used only if width was specified.

PiperOrigin-RevId: 332553548
Change-Id: Ie57cf2a7c14d1a36097642794c14329db669bbba


Vulnerable code:
      strings::Appendf(&format_, "%s%d", fill_string.c_str(), width);

Fixed code:
tf_cc_test(
    name = "as_string_op_test",
    size = "small",
    srcs = ["as_string_op_test.cc"],
    deps = [
        ":as_string_op",
        ":ops_testutil",
        ":ops_util",
        "//tensorflow/core:core_cpu",
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core:testlib",
    ],
)


    if (!fill_string.empty()) {
      switch (fill_string[0]) {
        case ' ':
        case '+':
        case '-':
        case '0':
        case '#':
          strings::Appendf(&format_, "%s", fill_string.c_str());
          break;
        default:
          bool fill_not_supported = true;
          OP_REQUIRES(ctx, !fill_not_supported,
                      errors::InvalidArgument("Fill argument not supported: \"",
                                              fill_string, "\""));
      }
    }
      strings::Appendf(&format_, "%d", width);
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/core/framework/fake_input.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/lib/core/status_test_util.h"

namespace tensorflow {
namespace {

class AsStringGraphTest : public OpsTestBase {
 protected:
  Status Init(DataType input_type, const string& fill = "", int width = -1,
              int precision = -1, bool scientific = false,
              bool shortest = false) {
    TF_CHECK_OK(NodeDefBuilder("op", "AsString")
                    .Input(FakeInput(input_type))
                    .Attr("fill", fill)
                    .Attr("precision", precision)
                    .Attr("scientific", scientific)
                    .Attr("shortest", shortest)
                    .Attr("width", width)
                    .Finalize(node_def()));
    return InitOp();
  }
};

TEST_F(AsStringGraphTest, Int8) {
  TF_ASSERT_OK(Init(DT_INT8));

  AddInputFromArray<int8>(TensorShape({3}), {-42, 0, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
  test::FillValues<tstring>(&expected, {"-42", "0", "42"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, Int64) {
  TF_ASSERT_OK(Init(DT_INT64));

  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
  test::FillValues<tstring>(&expected, {"-42", "0", "42"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FloatDefault) {
  TF_ASSERT_OK(Init(DT_FLOAT));

  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
  test::FillValues<tstring>(
      &expected, {"-42.000000", "0.000000", "3.141590", "42.000000"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FloatScientific) {
  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
                    /*scientific=*/true));

  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
  test::FillValues<tstring>(&expected, {"-4.200000e+01", "0.000000e+00",
                                        "3.141590e+00", "4.200000e+01"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FloatShortest) {
  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
                    /*scientific=*/false, /*shortest=*/true));

  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
  test::FillValues<tstring>(&expected, {"-42", "0", "3.14159", "42"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FloatPrecisionOnly) {
  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/2));

  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
  test::FillValues<tstring>(&expected, {"-42.00", "0.00", "3.14", "42.00"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FloatWidthOnly) {
  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/5));

  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
  test::FillValues<tstring>(
      &expected, {"-42.000000", "0.000000", "3.141590", "42.000000"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, Float_5_2_Format) {
  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/5, /*precision=*/2));

  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
  test::FillValues<tstring>(&expected, {"-42.00", " 0.00", " 3.14", "42.00"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, Complex) {
  TF_ASSERT_OK(Init(DT_COMPLEX64, /*fill=*/"", /*width=*/5, /*precision=*/2));

  AddInputFromArray<complex64>(TensorShape({3}), {{-4, 2}, {0}, {3.14159, -1}});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
  test::FillValues<tstring>(
      &expected, {"(-4.00, 2.00)", "( 0.00, 0.00)", "( 3.14,-1.00)"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, Bool) {
  TF_ASSERT_OK(Init(DT_BOOL));

  AddInputFromArray<bool>(TensorShape({2}), {true, false});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({2}));
  test::FillValues<tstring>(&expected, {"true", "false"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, String) {
  Status s = Init(DT_STRING);
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(absl::StrContains(
      s.error_message(),
      "Value for attr 'T' of string is not in the list of allowed values"));
}

TEST_F(AsStringGraphTest, OnlyOneOfScientificAndShortest) {
  Status s = Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
                  /*scientific=*/true, /*shortest=*/true);
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(
      absl::StrContains(s.error_message(),
                        "Cannot select both scientific and shortest notation"));
}

TEST_F(AsStringGraphTest, NoShortestForNonFloat) {
  Status s = Init(DT_INT32, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
                  /*scientific=*/false, /*shortest=*/true);
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(absl::StrContains(
      s.error_message(),
      "scientific and shortest format not supported for datatype"));
}

TEST_F(AsStringGraphTest, NoScientificForNonFloat) {
  Status s = Init(DT_INT32, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
                  /*scientific=*/true);
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(absl::StrContains(
      s.error_message(),
      "scientific and shortest format not supported for datatype"));
}

TEST_F(AsStringGraphTest, NoPrecisionForNonFloat) {
  Status s = Init(DT_INT32, /*fill=*/"", /*width=*/-1, /*precision=*/5);
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(absl::StrContains(s.error_message(),
                                "precision not supported for datatype"));
}

TEST_F(AsStringGraphTest, LongFill) {
  Status s = Init(DT_INT32, /*fill=*/"asdf");
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(absl::StrContains(s.error_message(),
                                "Fill string must be one or fewer characters"));
}

TEST_F(AsStringGraphTest, FillWithZero) {
  TF_ASSERT_OK(Init(DT_INT64, /*fill=*/"0", /*width=*/4));

  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
  test::FillValues<tstring>(&expected, {"-042", "0000", "0042"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FillWithSpace) {
  TF_ASSERT_OK(Init(DT_INT64, /*fill=*/" ", /*width=*/4));

  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
  test::FillValues<tstring>(&expected, {" -42", "   0", "  42"});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FillWithChar1) {
  TF_ASSERT_OK(Init(DT_INT64, /*fill=*/"-", /*width=*/4));

  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
  TF_ASSERT_OK(RunOpKernel());
  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
  test::FillValues<tstring>(&expected, {"-42 ", "0   ", "42  "});
  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
}

TEST_F(AsStringGraphTest, FillWithChar3) {
  Status s = Init(DT_INT32, /*fill=*/"s");
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(
      absl::StrContains(s.error_message(), "Fill argument not supported"));
}

TEST_F(AsStringGraphTest, FillWithChar4) {
  Status s = Init(DT_INT32, /*fill=*/"n");
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_TRUE(
      absl::StrContains(s.error_message(), "Fill argument not supported"));
}

}  // end namespace
}  // end namespace tensorflow