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Severe, 6 weeks1496Projects326

1496 severe, 6 weeks · 326 projects

TensorFlowGHSA-3h8m-483j-7xxm

Heap out of bounds read in `RequantizationRange`

TensorFlow

CVE-2021-29569 · Published May 21, 2021 · updated Jul 8, 2026

Low2.5
Fix: upgrade to 2.1.4 or later (4 fixed versions below)
GitHub advisory

Impact

The implementation of tf.raw_ops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:

```python

import tensorflow as tf

input = tf.constant([1], shape=[1], dtype=tf.qint32)

input_max = tf.constant([], dtype=tf.float32)

input_min = tf.constant([], dtype=tf.float32)

tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)

`

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:

```cc

const float input_min_float = ctx->input(1).flat<float>()(0);

const float input_max_float = ctx->input(2).flat<float>()(0);

`

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.

Patches

We have patched the issue in GitHub commit [ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e).

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 versions

PackageAffectedFixed in
tensorflow
PyPI
< 2.1.42.1.4
>= 2.2.0, < 2.2.32.2.3
>= 2.3.0, < 2.3.32.3.3
>= 2.4.0, < 2.4.22.4.2
Details and references

### Impact The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs: ```python import tensorflow as tf input = tf.constant([1], shape=[1], dtype=tf.qint32) input_max = tf.constant([], dtype=tf.float32) input_min = tf.constant([], dtype=tf.float32) tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max) ``` 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: ```cc const float input_min_float = ctx->input(1).flat<float>()(0); const float input_max_float = ctx->input(2).flat<float>()(0); ``` 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. ### Patches We have patched the issue in GitHub commit [ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e). 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. ### 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 Ying Wang and Yakun Zhang of Baidu X-Team.

CVSS 3.1
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Severity from
GitHub (reviewed advisory)
Weakness
CWE-125
Also known as
BIT-tensorflow-2021-29569, CVE-2021-29569, PYSEC-2021-206, PYSEC-2021-497, PYSEC-2021-695

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