CVE-2021-29537: Heap buffer overflow in `QuantizedResizeBilinear`

First published: Fri May 14 2021(Updated: )

### 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.

Credit: security-advisories@github.com security-advisories@github.com

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