CVE-2021-29536: Heap buffer overflow in `QuantizedReshape`

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

python import tensorflow as tf

tensor = tf.constant([], dtype=tf.qint32) shape = tf.constant([], dtype=tf.int32) inputmin = tf.constant([], dtype=tf.float32) inputmax = tf.constant([], dtype=tf.float32)

tf.rawops.QuantizedReshape(tensor=tensor, shape=shape, inputmin=inputmin, inputmax=inputmax)

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

cc const auto& inputminfloattensor = ctx->input(2); ... const float inputminfloat = inputminfloattensor.flat<float>()(0); const auto& inputmaxfloattensor = ctx->input(3); ... const float inputmaxfloat = inputmaxfloattensor.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 a324ac84e573fba362a5e53d4e74d5de6729933e.

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 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.