Commit message: Ensure num_streams >= 0 in tf.raw_ops.BoostedTreesCreateQuantileStreamResource

PiperOrigin-RevId: 387452765
Change-Id: I9990c760e177fabca6a3b9b4612ceeaeeba51495
CVE: CVE-2021-37661
CWE: CWE-703
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. 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
File: quantile_ops.cc

  void Compute(OpKernelContext* context) override {
    // Only create one, if one does not exist already. Report status for all
    // other exceptions. If one already exists, it unrefs the new one.
    // An epsilon value of zero could cause performance issues and is therefore,
    // disallowed.
    const Tensor* epsilon_t;
    OP_REQUIRES_OK(context, context->input(kEpsilonName, &epsilon_t));
    float epsilon = epsilon_t->scalar<float>()();
    OP_REQUIRES(
        context, epsilon > 0,
        errors::InvalidArgument("An epsilon value of zero is not allowed."));

    const Tensor* num_streams_t;
    OP_REQUIRES_OK(context, context->input(kNumStreamsName, &num_streams_t));
    int64_t num_streams = num_streams_t->scalar<int64>()();

    auto result =
        new QuantileStreamResource(epsilon, max_elements_, num_streams);
    auto status = CreateResource(context, HandleFromInput(context, 0), result);
    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
      OP_REQUIRES(context, false, status);
    }
  }

Project: tensorflow
File: quantile_ops.cc

  void Compute(OpKernelContext* context) override {
    // Only create one, if one does not exist already. Report status for all
    // other exceptions. If one already exists, it unrefs the new one.
    // An epsilon value of zero could cause performance issues and is therefore,
    // disallowed.
    const Tensor* epsilon_t;
    OP_REQUIRES_OK(context, context->input(kEpsilonName, &epsilon_t));
    float epsilon = epsilon_t->scalar<float>()();
    OP_REQUIRES(
        context, epsilon > 0,
        errors::InvalidArgument("An epsilon value of zero is not allowed."));

    const Tensor* num_streams_t;
    OP_REQUIRES_OK(context, context->input(kNumStreamsName, &num_streams_t));
    int64_t num_streams = num_streams_t->scalar<int64>()();
    OP_REQUIRES(context, num_streams >= 0,
                errors::InvalidArgument(
                    "Num_streams input cannot be a negative integer"));

    auto result =
        new QuantileStreamResource(epsilon, max_elements_, num_streams);
    auto status = CreateResource(context, HandleFromInput(context, 0), result);
    if (!status.ok() && status.code() != tensorflow::error::ALREADY_EXISTS) {
      OP_REQUIRES(context, false, status);
    }
  }