TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
History

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cve-icon MITRE

Status: PUBLISHED

Assigner: GitHub_M

Published: 2021-05-14T19:16:36

Updated: 2024-08-03T22:11:05.766Z

Reserved: 2021-03-30T00:00:00

Link: CVE-2021-29569

cve-icon Vulnrichment

No data.

cve-icon NVD

Status : Analyzed

Published: 2021-05-14T20:15:13.790

Modified: 2021-05-20T14:56:28.580

Link: CVE-2021-29569

cve-icon Redhat

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