Null pointer dereference in `SparseFillEmptyRows`

Overview

Impact

An attacker can trigger a null pointer dereference in the implementation of tf.raw_ops.SparseFillEmptyRows:

import tensorflow as tf

indices = tf.constant([], shape=[0, 0], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
dense_shape = tf.constant([], shape=[0], dtype=tf.int64)
default_value = 0
    
tf.raw_ops.SparseFillEmptyRows(
    indices=indices, values=values, dense_shape=dense_shape,
    default_value=default_value)

This is because of missing validation that was covered under a TODO. If the dense_shape tensor is empty, then dense_shape_t.vec<>() would cause a null pointer dereference in the implementation of the op:

template <typename T, typename Tindex>
struct SparseFillEmptyRows<CPUDevice, T, Tindex> {
  Status operator()(OpKernelContext* context, const Tensor& default_value_t,
                    const Tensor& indices_t, const Tensor& values_t,
                    const Tensor& dense_shape_t,
                    typename AsyncOpKernel::DoneCallback done) {
    ...
    const auto dense_shape = dense_shape_t.vec<Tindex>();
    ... 
  }
}

Patches

We have patched the issue in GitHub commit faa76f39014ed3b5e2c158593b1335522e573c7f.

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 Yakun Zhang and Ying Wang of Baidu X-Team.

Affected packages

• tensorflow < 2.1.4
• tensorflow >= 2.2.0, < 2.2.3
• tensorflow >= 2.3.0, < 2.3.3
• tensorflow >= 2.4.0, < 2.4.2
• tensorflow-cpu < 2.1.4
• tensorflow-cpu >= 2.2.0, < 2.2.3
• tensorflow-cpu >= 2.3.0, < 2.3.3
• tensorflow-cpu >= 2.4.0, < 2.4.2
• tensorflow-gpu < 2.1.4
• tensorflow-gpu >= 2.2.0, < 2.2.3
• tensorflow-gpu >= 2.3.0, < 2.3.3
• tensorflow-gpu >= 2.4.0, < 2.4.2

Remediation

Upgrade to a patched release:

• tensorflow 2.1.4
• tensorflow 2.2.3
• tensorflow 2.3.3
• tensorflow 2.4.2
• tensorflow-cpu 2.1.4
• tensorflow-cpu 2.2.3
• tensorflow-cpu 2.3.3
• tensorflow-cpu 2.4.2
• tensorflow-gpu 2.1.4
• tensorflow-gpu 2.2.3
• tensorflow-gpu 2.3.3
• tensorflow-gpu 2.4.2