GHSA-gf88-j2mg-cc82

Impact

An attacker can cause a denial of service in boosted_trees_create_quantile_stream_resource by using negative arguments:

import tensorflow as tf
from tensorflow.python.ops import gen_boosted_trees_ops
import numpy as np

v= tf.Variable([0.0, 0.0, 0.0, 0.0, 0.0])
gen_boosted_trees_ops.boosted_trees_create_quantile_stream_resource(
  quantile_stream_resource_handle = v.handle,
  epsilon = [74.82224],
  num_streams = [-49], 
  max_elements = np.int32(586))

The implementation does not validate that num_streams only contains non-negative numbers. In turn, this results in using this value to allocate memory:

class BoostedTreesQuantileStreamResource : public ResourceBase {
 public:
  BoostedTreesQuantileStreamResource(const float epsilon,
                                     const int64 max_elements,
                                     const int64 num_streams)
      : are_buckets_ready_(false),
        epsilon_(epsilon),
        num_streams_(num_streams),
        max_elements_(max_elements) {
    streams_.reserve(num_streams_);
    ...
  }
}

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.

Patches

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.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.