CVE-2021-29569

Description

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.

Weakness classification

• security-advisories@github.com: CWE-125

Primary references

• security-advisories@github.com — Patch, Third Party Advisory
• security-advisories@github.com — Exploit, Patch, Third Party Advisory
• af854a3a-2127-422b-91ae-364da2661108 — Patch, Third Party Advisory
• af854a3a-2127-422b-91ae-364da2661108 — Exploit, Patch, Third Party Advisory