Crash caused by integer conversion to unsigned

### Impact An attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments: ```python 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](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): ```cc 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](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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.

CVSS 3.1

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Severity from

GitHub (reviewed advisory)

Weakness

CWE-681

Also known as

BIT-tensorflow-2021-37661, CVE-2021-37661, PYSEC-2021-283, PYSEC-2021-574, PYSEC-2021-772

• github.com/tensorflow/tensorflow/security/advisories/GHSA-gf88-j2mg-cc82
• nvd.nist.gov/vuln/detail/CVE-2021-37661
• github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-574.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-772.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-283.yaml
• github.com/tensorflow/tensorflow