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GHSA-9c8h-vvrj-w2p8
No affected components available
Impact
If the arguments to tf.raw_ops.RaggedGather don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers.
import tensorflow as tf
tf.raw_ops.RaggedGather(
params_nested_splits = [0,0,0],
params_dense_values = [1,1],
indices = [0,0,9,0,0],
OUTPUT_RAGGED_RANK=0)
In debug mode, the same code triggers a CHECK failure.
The implementation directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by params_nested_splits is not an empty list of tensors.
Patches
We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 confidentiality of the information. 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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