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GHSA-h67m-xg8f-fxcf
No affected components available
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.
The vulnerability requires local access to the device to be exploited. It is easy for an attacker to exploit this vulnerability. An attacker needs basic access or low-level privileges. No user interaction is needed for the attacker to exploit this vulnerability. The impact is confined to the system where the vulnerability exists. There is a high impact on the availability of the system.
Exploitation attempts have been detected. Elevated vigilance and prompt remediation are advised.
The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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