cve-2021-37661
Vulnerability from cvelistv5
Published
2021-08-12 21:05
Modified
2024-08-04 01:23
Summary
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.
Impacted products
Vendor Product Version
Show details on NVD website


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La [implementaci\u00f3n](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) no comprueba que \\\"num_streams\\\" s\u00f3lo contenga n\u00fameros no negativos. A su vez, [esto resulta en usar este valor para asignar memoria](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). Sin embargo, \\\"reserve\\\" recibe un entero sin signo, por lo que se presenta una conversi\u00f3n impl\u00edcita de un valor negativo a un grande positivo sin signo. Esto resulta en un bloqueo de la biblioteca est\u00e1ndar. Hemos parcheado el problema en el commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992 de GitHub. La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.6.0. 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  }
}


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