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Severe, 6 weeks1496Projects326

1496 severe, 6 weeks · 326 projects

TensorFlowGHSA-8c89-2vwr-chcq

Heap buffer overflow in `QuantizedResizeBilinear`

TensorFlow

CVE-2021-29537 · Published May 21, 2021 · updated Sep 10, 2026

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

Impact

An attacker can cause a heap buffer overflow in QuantizedResizeBilinear by passing in invalid thresholds for the quantization:

```python

import tensorflow as tf

images = tf.constant([], shape=[0], dtype=tf.qint32)

size = tf.constant([], shape=[0], dtype=tf.int32)

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

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

tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=False, half_pixel_centers=False)

`

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:

```cc

const float in_min = context->input(2).flat<float>()(0);

const float in_max = context->input(3).flat<float>()(0);

`

However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.

Patches

We have patched the issue in GitHub commit [f6c40f0c6cbf00d46c7717a26419f2062f2f8694](https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694).

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...

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 An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization: ```python import tensorflow as tf images = tf.constant([], shape=[0], dtype=tf.qint32) size = tf.constant([], shape=[0], dtype=tf.int32) min = tf.constant([], dtype=tf.float32) max = tf.constant([], dtype=tf.float32) tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=False, half_pixel_centers=False) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly: ```cc const float in_min = context->input(2).flat<float>()(0); const float in_max = context->input(3).flat<float>()(0); ``` However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. ### Patches We have patched the issue in GitHub commit [f6c40f0c6cbf00d46c7717a26419f2062f2f8694](https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694). 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-131, CWE-787
Also known as
BIT-tensorflow-2021-29537, CVE-2021-29537, PYSEC-2021-174, PYSEC-2021-465, PYSEC-2021-663

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