CVE-2021-29569: Heap out of bounds read in `RequantizationRange`

Severity: high (CVSS 7.1 v3.1)
Exploited in the wild: not listed in CISA KEV
EPSS: 0.2% probability of exploitation in the next 30 days
Public exploit or detection template: yes
Published: 2021-05-14 · updated 2026-06-17
Weakness: CWE-125

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.

Affected products

tensorflow: before 2.1.4; from 2.2.0 before 2.2.3; from 2.3.0 before 2.3.3; from 2.4.0 before 2.4.2 — fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2

How to fix

Upgrade to a fixed version: tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2. Check the vendor advisory for mitigations.

References

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