Know every vulnerabilitybefore it knows you.
DevGuard continuously monitors your dependencies and alerts you when CVEs like this one affect your stack — with real-time threat intelligence built for developers.
GHSA-f2w8-jw48-fr7j
No affected components available
Impact
If FractionMaxPoolGrad is given outsize inputs row_pooling_sequence and col_pooling_sequence, TensorFlow will crash.
import tensorflow as tf
tf.raw_ops.FractionMaxPoolGrad(
orig_input = [[[[1, 1, 1, 1, 1]]]],
orig_output = [[[[1, 1, 1]]]],
out_backprop = [[[[3], [3], [6]]]],
row_pooling_sequence = [-0x4000000, 1, 1],
col_pooling_sequence = [-0x4000000, 1, 1],
overlapping = False
)
Patches
We have patched the issue in GitHub commit d71090c3e5ca325bdf4b02eb236cfb3ee823e927.
The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 Vul AI.
The vulnerability can be exploited over the network without needing physical access. It is difficult for an attacker to exploit this vulnerability and may require special conditions. An attacker needs basic access or low-level privileges. The attacker needs the user to perform some action, like clicking a link. 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.
Browse More
Continuously monitor your dependencies and get alerted when vulnerabilities like this one affect your stack.
Checkout DevGuard