CVE-2021-41213

TensorFlow is an open source platform for machine learning. In affected versions the code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive. This occurs due to using a non-reentrant Lock Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive. This occurs due to using a non-reentrant Lock Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Impact

The code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive:

import tensorflow as tf

@tf.function()               
def fun1(num):
    if num == 1:
        return
    print(num)
    fun2(num-1)

@tf.function()
def fun2(num):
    if num == 0:
        return
    print(num)
    fun1(num-1)

fun1(9)

This occurs due to using a non-reentrant Lock Python object.

Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario.

Patches

We have patched the issue in GitHub commit afac8158d43691661ad083f6dd9e56f327c1dcb7.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.

TensorFlow is an open source platform for machine learning. In affected versions the code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive. This occurs due to using a non-reentrant Lock Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive. This occurs due to using a non-reentrant Lock Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Impact

The code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive:

import tensorflow as tf

@tf.function()               
def fun1(num):
    if num == 1:
        return
    print(num)
    fun2(num-1)

@tf.function()
def fun2(num):
    if num == 0:
        return
    print(num)
    fun1(num-1)

fun1(9)

This occurs due to using a non-reentrant Lock Python object.

Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario.

Patches

We have patched the issue in GitHub commit afac8158d43691661ad083f6dd9e56f327c1dcb7.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.