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GHSA-fxqh-cfjm-fp93
No affected components available
Impact
An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse:
import tensorflow as tf
tensor_input = tf.constant([], shape=[0, 1, 1], dtype=tf.int32)
dims = tf.constant([False, True, False], shape=[3], dtype=tf.bool)
tf.raw_ops.Reverse(tensor=tensor_input, dims=dims)
This is because the implementation performs a division based on the first dimension of the tensor argument:
const int64 N = input.dim_size(0);
const int64 cost_per_unit = input.NumElements() / N;
Since this is controlled by the user, an attacker can trigger a denial of service.
Patches
We have patched the issue in GitHub commit 4071d8e2f6c45c1955a811fee757ca2adbe462c1.
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 Ying Wang and Yakun Zhang of Baidu X-Team.
The vulnerability requires local access to the device to be exploited. It is difficult for an attacker to exploit this vulnerability and may require special conditions. 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 low impact on the availability of the system.
Limited exploitation activity has been observed. Close monitoring and planned remediation are recommended.
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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